IOSR Journal of Mathematics(IOSR-JM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A new CPXR Based Logistic Regression Method and Clinical Prognostic Modeling ...Vahid Taslimitehrani
Presented at 15th International Conference on BioInformatics and BioEngineering (BIBE2014)
Prognostic modeling is central to medicine, as it is often used to predict patients’ outcome and response to treatments and to identify important medical risk factors. Logistic regression is one of the most used approaches for clinical pre- diction modeling. Traumatic brain injury (TBI) is an important public health issue and a leading cause of death and disability worldwide. In this study, we adapt CPXR (Contrast Pattern Aided Regression, a recently introduced regression method), to develop a new logistic regression method called CPXR(Log), for general binary outcome prediction (including prognostic modeling), and we use the method to carry out prognostic modeling for TBI using admission time data. The models produced by CPXR(Log) achieved AUC as high as 0.93 and specificity as high as 0.97, much better than those reported by previous studies. Our method produced interpretable prediction models for diverse patient groups for TBI, which show that different kinds of patients should be evaluated differently for TBI outcome prediction and the odds ratios of some predictor variables differ significantly from those given by previous studies; such results can be valuable to physicians.
APPROACHES IN USING EXPECTATIONMAXIMIZATION ALGORITHM FOR MAXIMUM LIKELIHOOD ...cscpconf
EM algorithm is popular in maximum likelihood estimation of parameters for state-space models. However, extant approaches for the realization of EM algorithm are still not able to fulfill the task of identification systems, which have external inputs and constrained parameters. In this paper, we propose new approaches for both initial guessing and MLE of the parameters of a constrained state-space model with an external input. Using weighted least square for the initial guess and the partial differentiation of the joint log-likelihood function for the EM algorithm, we estimate the parameters and compare the estimated values with the “actual” values, which are set to generate simulation data. Moreover, asymptotic variances of the estimated parameters are calculated when the sample size is large, while statistics of the estimated parameters are obtained through bootstrapping when the sample size issmall. The results demonstrate that the estimated values are close to the “actual” values.Consequently, our approaches are promising and can applied in future research.
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Elementary Statistics Practice Test 4
Module 4:
Chapter 8, Hypothesis Testing
Chapter 9: Two Populations
A new CPXR Based Logistic Regression Method and Clinical Prognostic Modeling ...Vahid Taslimitehrani
Presented at 15th International Conference on BioInformatics and BioEngineering (BIBE2014)
Prognostic modeling is central to medicine, as it is often used to predict patients’ outcome and response to treatments and to identify important medical risk factors. Logistic regression is one of the most used approaches for clinical pre- diction modeling. Traumatic brain injury (TBI) is an important public health issue and a leading cause of death and disability worldwide. In this study, we adapt CPXR (Contrast Pattern Aided Regression, a recently introduced regression method), to develop a new logistic regression method called CPXR(Log), for general binary outcome prediction (including prognostic modeling), and we use the method to carry out prognostic modeling for TBI using admission time data. The models produced by CPXR(Log) achieved AUC as high as 0.93 and specificity as high as 0.97, much better than those reported by previous studies. Our method produced interpretable prediction models for diverse patient groups for TBI, which show that different kinds of patients should be evaluated differently for TBI outcome prediction and the odds ratios of some predictor variables differ significantly from those given by previous studies; such results can be valuable to physicians.
APPROACHES IN USING EXPECTATIONMAXIMIZATION ALGORITHM FOR MAXIMUM LIKELIHOOD ...cscpconf
EM algorithm is popular in maximum likelihood estimation of parameters for state-space models. However, extant approaches for the realization of EM algorithm are still not able to fulfill the task of identification systems, which have external inputs and constrained parameters. In this paper, we propose new approaches for both initial guessing and MLE of the parameters of a constrained state-space model with an external input. Using weighted least square for the initial guess and the partial differentiation of the joint log-likelihood function for the EM algorithm, we estimate the parameters and compare the estimated values with the “actual” values, which are set to generate simulation data. Moreover, asymptotic variances of the estimated parameters are calculated when the sample size is large, while statistics of the estimated parameters are obtained through bootstrapping when the sample size issmall. The results demonstrate that the estimated values are close to the “actual” values.Consequently, our approaches are promising and can applied in future research.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Elementary Statistics Practice Test 4
Module 4:
Chapter 8, Hypothesis Testing
Chapter 9: Two Populations
Dem 7263 fall 2015 spatially autoregressive models 1Corey Sparks
These are notes for my Spatial Demography course. This lecture deals with the spatially autoregressive model. The model is reviewed and several applications are shown using real data for San Antonio, TX an US counties
THE EFFECT OF SEGREGATION IN NONREPEATED PRISONER'S DILEMMA ijcsit
This article consolidates the idea that non-random pairing can promote the evolution of cooperation in a non-repeated version of the prisoner’s dilemma. This idea is taken from[1], which presents experiments utilizing stochastic simulation. In the following it is shown how the results from [1] is reproducible by
numerical analysis. It is also demonstrated that some unexplained findings in [1], is due to the methods used.
Credit Rating Process with Respect to Corporate DebtSumit Kumar Singh
Volatility in the financial market is becoming common day by day as we are becoming more and more intensive towards global market. The importance of Credit Rating Agencies has gone up because an investor can't always keep track on key 'Financial Metrics' of companies. So investors try to fix this with the help of ratings assigned by Recognized Rating Agencies
Dem 7263 fall 2015 spatially autoregressive models 1Corey Sparks
These are notes for my Spatial Demography course. This lecture deals with the spatially autoregressive model. The model is reviewed and several applications are shown using real data for San Antonio, TX an US counties
THE EFFECT OF SEGREGATION IN NONREPEATED PRISONER'S DILEMMA ijcsit
This article consolidates the idea that non-random pairing can promote the evolution of cooperation in a non-repeated version of the prisoner’s dilemma. This idea is taken from[1], which presents experiments utilizing stochastic simulation. In the following it is shown how the results from [1] is reproducible by
numerical analysis. It is also demonstrated that some unexplained findings in [1], is due to the methods used.
Credit Rating Process with Respect to Corporate DebtSumit Kumar Singh
Volatility in the financial market is becoming common day by day as we are becoming more and more intensive towards global market. The importance of Credit Rating Agencies has gone up because an investor can't always keep track on key 'Financial Metrics' of companies. So investors try to fix this with the help of ratings assigned by Recognized Rating Agencies
DETECTION OF RELIABLE SOFTWARE USING SPRT ON TIME DOMAIN DATAIJCSEA Journal
In Classical Hypothesis testing volumes of data is to be collected and then the conclusions are drawn which may take more time. But, Sequential Analysis of statistical science could be adopted in order to decide upon the reliable / unreliable of the developed software very quickly. The procedure adopted for this is, Sequential Probability Ratio Test (SPRT). In the present paper we proposed the performance of SPRT on Time domain data using Weibull model and analyzed the results by applying on 5 data sets. The parameters are estimated using Maximum Likelihood Estimation.
Design of optimized Interval Arithmetic MultiplierVLSICS Design
Many DSP and Control applications that require the user to know how various numerical errors(uncertainty) affect the result. This uncertainty is eliminated by replacing non-interval values with intervals. Since most DSPs operate in real time environments, fast processors are required to implement interval arithmetic. The goal is to develop a platform in which Interval Arithmetic operations are performed at the same computational speed as present day signal processors. So we have proposed the design and implementation of Interval Arithmetic multiplier, which operates with IEEE 754 numbers. The proposed unit consists of a floating point CSD multiplier, Interval operation selector. This architecture implements an algorithm which is faster than conventional algorithm of Interval multiplier . The cost overhead of the proposed unit is 30% with respect to a conventional floating point multiplier. The
performance of proposed architecture is better than that of a conventional CSD floating-point multiplier, as it can perform both interval multiplication and floating-point multiplication as well as Interval comparisons
An Efficient Unsupervised AdaptiveAntihub Technique for Outlier Detection in ...theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
An overview of the significance of SURE(Seemingly unrelated regression) model in Panel data econometrics and its applications.
The presentation consists of the theoretical background and mathematical derivation for the model. The stochastic frontier model and treatment effects are also discussed in brief.
A Study on Performance Analysis of Different Prediction Techniques in Predict...IJRES Journal
Time series data is a series of statistical data that is related to a specific instant or a specific time period. Here, the measurements are recorded on a regular basis such as monthly, quarterly and yearly. Most of the researchers have used one of the prediction techniques in prediction of time series data. But, they have not tested all prediction techniques on same data set. They have not even compared the performance of different prediction techniques on the same data set. In this research work, some well known prediction techniques have been applied in the same time series data set. The average error and residual analysis have been done for each and every applied technique. One technique has been selected based on the minimum average error and residual analysis among the all applied techniques. The residual analysis comprises of absolute residual, maximum residual, median of absolute residual, mean of absolute residual and standard deviation. To finalize the algorithm, same procedure has been applied on different time series data sets. Finally, one technique has been selected which has been given minimum error and minimum value of residual analysis in most cases.
Neural Network based Supervised Self Organizing Maps for Face Recognition ijsc
The word biometrics refers to the use of physiological or biological characteristics of human to recognize and verify the identity of an individual. Face is one of the human biometrics for passive identification with uniqueness and stability. In this manuscript we present a new face based biometric system based on neural networks supervised self organizing maps (SOM). We name our method named SOM-F. We show that the proposed SOM-F method improves the performance and robustness of recognition. We apply the proposed method to a variety of datasets and show the results.
Similar to Identification of Outliersin Time Series Data via Simulation Study (20)
An Examination of Effectuation Dimension as Financing Practice of Small and M...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Does Goods and Services Tax (GST) Leads to Indian Economic Development?iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Childhood Factors that influence success in later lifeiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Emotional Intelligence and Work Performance Relationship: A Study on Sales Pe...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Customer’s Acceptance of Internet Banking in Dubaiiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Study of Employee Satisfaction relating to Job Security & Working Hours amo...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Consumer Perspectives on Brand Preference: A Choice Based Model Approachiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Student`S Approach towards Social Network Sitesiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Broadcast Management in Nigeria: The systems approach as an imperativeiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Study on Retailer’s Perception on Soya Products with Special Reference to T...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Study Factors Influence on Organisation Citizenship Behaviour in Corporate ...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Consumers’ Behaviour on Sony Xperia: A Case Study on Bangladeshiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Design of a Balanced Scorecard on Nonprofit Organizations (Study on Yayasan P...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Public Sector Reforms and Outsourcing Services in Nigeria: An Empirical Evalu...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Media Innovations and its Impact on Brand awareness & Considerationiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Customer experience in supermarkets and hypermarkets – A comparative studyiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Social Media and Small Businesses: A Combinational Strategic Approach under t...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Secretarial Performance and the Gender Question (A Study of Selected Tertiary...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Implementation of Quality Management principles at Zimbabwe Open University (...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Organizational Conflicts Management In Selected Organizaions In Lagos State, ...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Identification of Outliersin Time Series Data via Simulation Study
1. IOSR Journal of Mathematics (IOSR-JM)
e-ISSN: 2278-5728, p-ISSN: 2319-765X. Volume 11, Issue 6 Ver. V (Nov. - Dec. 2015), PP 60-67
www.iosrjournals.org
DOI: 10.9790/5728-11656067 www.iosrjournals.org 60 | Page
Identification of Outliersin Time Series Data via
Simulation Study
1
ADEWALE Asiata. O,2
Nurul Sima MOHAMAD SHARIFF
1,2,
Facultyof Science&TechnologyUniversitySainsIslamMalaysia (USIM)Bandar Baru Nilai, 71800 Nilai
Negeri SembilanMalaysia
Abstract:This paper compares the performance of regression diagnostics techniques based on
Ordinary LeastSquares (OLS) estimators and four types of robust regression based on robust estimators
todetect and identify outliers. It is known that OLS is not robust in the presence of multiple high leverage
points.Thus severalrobust regression models are used as alternative and its approach is more reliable and
appropriate method for solving this problem. The comparisonsaremadeviasimulationstudies.Our
resultshaveshownthatinsomecases diagnostics basedonthe OLSandsome
robustestimatorsgivesimilaroutcomes,they detectthesame percentage of correct outlier detection. And under
small sample size OLS and M-estimation perform best for innovative outliers. The results also shows that Least
Trimmed Square is the best among all its counterparts under large sample size.
Keywords: Outliers, Ordinary Least Squares (OLS), Regression diagnostics, Robust regression,
Simulation Studies.
I. Introduction
Outliers are usually encountered in time series data analysis. The presence of outliers in time series
analysis can seriously has negative impact in the analysis because they may stimulate substantial biasness in
parameter estimation, model misspecification and incorrect inference, [1]. Outliers has been defined by Abd.
Mutalib and Ibrahim [2] as data points or observations that deviate distinctly from other observations or data
points which are abnormally large or small from the other observations. The relevancies of outlier detection and
identification in time series have been used in fraud detection, financial institute, public health and
Telecommunication Company. According to Lopez-de-Lacalle[3], there are five types of outliers that are
commonly found, namely, innovation outlier (IO), additive outlier (AO), level shift (LS), temporary change
(TC)and seasonal level shift (SLS).
The most popular way to analyse time series regression model is to use Ordinary Least Square (OLS)
method. It is the best technique if all the statistical assumptions are valid but when the data or the series are
contaminated with outliers, these statistical assumptions are invalid. There arise the needs of regression
diagnostics tools or techniques to detect and identify the outliers or influential observations. There are many
types of regression diagnostics tools in the literature, among them are: The welsch-kuh distances, Cook’s
Distance and Hadis influence Measure. However, these methods are not robust in presence of multiple high
leverage point, which can cause masking and swamping effects [4]. According to Widodo et al [5], robust
regression approach is more reliable and appropriate method for solving this problem. The robust estimators are
relatively unaltered by large changes in a small series of data and also small changes in a large part of the series.
Yafee [6] discusses that there are several kinds of robust estimators in the literature among which are Least
Absolute Deviations (LAD or L1), Least Median Squares (LMS), Least Trimmed Squares (LTS) and Huber M-
Estimation. These robust estimators will be used in this research work by S plus statistical software through
simulation study. Their performance will be compared to one another and the best technique among them will
be figured determined.
II. Materials and Method
OLS Estimation
Consider the time series model of simple autoregressive, AR(p)
tptptt yyy ...11 (1)
The time series model of simple autoregressive, AR(p) as the sameform as (1), can be written as
,...210
pii
xy ni ,...,2,1 (1)
Model (2) can be written in matrix form as,
XY (3)
2. Identification of Outliersin Time Series Data via...
DOI: 10.9790/5728-11656067 www.iosrjournals.org 61 | Page
WhereY= vector of nx1 response,Xis the n x p matrix of explanatory variables, is the vector of parameter
(regression coefficient), is the random error distributed as normal distribution with mean zero and
2
. Here
n is number of observations and p is number of regressors.
The final estimates of is then,
YXXX TT 1
)(ˆ
(4)
The residual isobtained as follows:
yHYXXXXYXYYYe TT
)1()(ˆˆ 1
(5)
Where
TT
XXXXH 1
)(
is leverage / hat matrix.
The i-th elements of H is,
T
i
T
iii xXXxh 1
)(
(6)
Regression Diagnostics Methods
There is numerous regression diagnostics methods used to identify outliers. However, this study will only
consider five methods that are listed below.
Hadi’s influence measure
,
111 2
2
2
i
i
iiii
ii
i
d
d
h
p
h
h
H
ni ,...,3,2,1 (7)
Where
SSE
e
d i
i 2
the normalized, SSE is sum squares residual residuals, It identify influential observations.
The cut-off point for
2
iH measure is )var()( 22
ii HCHmean C is constant which take the value of 2.
The welsch-kuh distances
,
)1(ˆ
*
)( iii
iii
h
he
DFFits
ni ,...,3,2,1 (8)
has the cuff-off value of
n
p
*2
Cook’s Distance
,
)1)(1(
*2
ii
iii
i
hp
hr
D
ni ,...,3,2,1 (9)
Where
i
i
i
h
e
r
1ˆ
, Any observation that exceeds the cut-off of
pn
4
is considered as influential
observation.
Robust Regression
Robust regression methods are designed not to be wholly affected by violations of assumptions by the core data
generating process. A robust regression is performed on a high breakdown point and high efficiency regression,
[9].
Huber M-Estimation
Huber-M estimation uses Huber weight function as its weight function. The Huber M-Estimator scale estimate
of mˆ and the Huber M-Estimator error me are usedinstead ofˆ , and ie which are based on OLS method.
3. Identification of Outliersin Time Series Data via...
DOI: 10.9790/5728-11656067 www.iosrjournals.org 62 | Page
Least absolute deviations (LAD or L1)
LAD obtains a higher effectiveness than OLS through minimizing the sum of the absolute errors, [7]. It scale
estimate denoted by 1
ˆL and its error 1Le are usedinstead ofˆ , and ie which are based on OLS.
Least median squares (LMS)
LMS is a robust estimator that has been hypothetically has breakdown point of 50%, [8]. This means the LMS
provides reliable outcomes even if up to 50% of contaminated data or series exist. It has the characteristics of
solving the liner model by minimising the median of the weighted squared. LMS scale estimate denoted by
LMSˆ and its error LMSe are usedinstead ofˆ , and ie which are based on OLS.
Least trimmed squares (LTS)
This robust regression techniques minimizes the sum of squared residuals over n observations and subset k of
those observations, thus the n-k observations which are excluded does not has effect on the fit.The LAD scale
estimate is denoted by LADˆ and its error LADe are usedinstead ofˆ , and ie which are based on OLS.
Note that their cuff-value remains the same as the OLS.
Incorporating outliers into the series.
From the original series, model1:
)( ttt Iyq , the series is contaminated with outliers.
is the magnitude of the outliers, is the time that the outliers occur and )(tI is a dummy variable which has
zero value at all lags except when time Tt
t
t
It
,0
,1
tatoccursioncontaminatwhen , 1tI otherwise 0.
III. Result and Discussion
The sample size used is n=30 & 200, parameter is set to be 7.0 , 5 , standard deviation 1 ,300
replications for n=30 and 500 replications for n = 200. To assess the power of the procedure, the following case
will be considered;
i. Single outlier of AO / IO
ii. Multiple outliers AOs / IOs
iii. Both multiple outliers AOs and IOs
Fifthteen measures will be run for each cases,
Under n=30, the location for single outlier is set to be 12 and for the multiple outlier, the location is set to
be k1 =12, and k= 20. Forn=200, the location for single outlier is set to be 26 , and multiple outliers; k1
=26, k2 =62 and k3 =99.
Table 1. A simulation study on the power of the outlier detection in regression diagnostics tools based on
ols.
12 12 (n=30,ai=0.7,nsimul=300,p=1,k1=12,k2=20)
Case Single
AO
Single
IO
2 AOs 2IOs Both AO and IO
1st
outlier
2nd
outlier
1st
outlier
2nd
outlier
1st
outlier
(IO)
2nd
outlier
(AO)
Ordinary Least Square (OLS)
2
iH 94.6 99.3 80 80 86.3 87.3 99.3 0.3
Dffits 87.7 99.7 70.7 79 97.7 98 99.6 0.3
4. Identification of Outliersin Time Series Data via...
DOI: 10.9790/5728-11656067 www.iosrjournals.org 63 | Page
Di 82.7 99 54 61 92.7 93 99 0.3
Summary of the outliers detection performance. Note that the numbers are in percentage.
Table 1. Present the result of the performance of 300 replications on outlier-detection method for each single
and multiple outlier specifications using the cut-off C value of each classical diagnostic techniques as stated
earlier respectively. The numbers and percentage of the correct detection and identification are given under each
location of the outliers. Under multiple outlier and “Both AO and IO”, the label k1 and k2, tells us the location
of 1st outlier, outlier and 2nd outlier respectively, and for single outlier.
The result shows that OLS based diagnostic technique, 2
iH detect 94.6% of correct number of outlier, follow by
Dffits (87.7%) and iD , (82.7%) under single AO while in single IO, the percentage detection for Dffits , 2
iH and
iD is very powerful i.e (99.7%, 99.3% and 99%). For multiple IO (2IOs), there seems to be a better correct
detection percentage ranging from 86.3% to 99.6%, and for AO (2AOs), 54% to 80%. The result outcome of
“Both AO and IO” seems to favour additive outliers (IO) and perform woefully under the additive outlier (AO),
this may be that OLS based diagnostic technique can only detect correctly the first outlier that comes on it way
and swamp the rest of the outlier.
Table 2. A simulation study on the power of the outlier detection based on robust versions of
regression diagnostics tools.
12 12 (n=30,ai=0.7,nsimul=300,p=1,k1=12,k2=20)
case Single
AO
Single
IO
2 AOs 2IOs Both AO and IO
1st
outlier
2nd
outlier
1st
outlier
2nd
outlier
1st
outlier
(IO)
2nd
outlier
(AO)
M estimation
2
iH 94.6 99 80.3 82.3 88.3 91.7 99 0.3
Dffits 86.3 99.7 71.2 78.3 97.7 98.7 99.6 0.3
Di 84 99.3 53.3 61 92.3 92.7 99.3 0.3
L1/Least Absolute Deviation / Least Absolute value
2
iH 84.3 98.3 76.7 77.3 88.3 92 98.3 0.3
Dffits 77.7 99.3 58 64.3 95.3 95 96.3 0.3
Di 71 95.7 45 52 90 90.3 95.7 0.3
Least Median Square
2
iH 87.3 96 82.7 86 84 85 96 0.3
Dffits 75.3 83 63.3 68.3 83 84.3 82 0.3
Di 69 79.7 56.3 56 79.3 81 79.7 0.3
Least Trimmed Square
2
iH 87.3 98 73.7 73 89.7 90.7 98 0.3
Dffits 79.3 91.7 63 65.7 91.3 92.7 91.7 0.3
Di 71 85.7 43 48.3 84.7 85.7 85.7 0.3
Summary of the outliers detection performance. Note that the numbers are in percentage.
Table 2. present the results of the proposed methods based on robust version which contains four different kinds
of robust regression namely M-estimation, LAD/L1, LMS and LTS respectively, the table shows the comparison
between their power of performance accordingly in the correctly detection and identification in percentage.
5. Identification of Outliersin Time Series Data via...
DOI: 10.9790/5728-11656067 www.iosrjournals.org 64 | Page
Which one has the most powerful performance among the robust regressions, however their outliers’ locations
and cut-off values C are set as the same in Table 1. The interpretations of the table are as follows:
a. M-estimation
The power of correct detection and identification percentage for single AO under 2
iH is 94.6% and, Dffits
(86.3%) and iD (84%). There is a very powerful correct percentage detection for single IO i.e. 99% to 99.7%.
Under multiple IO, the power of correct percentage detection and identification is between 88.3% to 97.7% for
2
iH , Dffits and iD . And for multiple AO, 2
iH , Dffits and iD has 99% to 99.6% power of correctly detection
and identification. The 1st
outlier in “both AO and IO”, which is IO has 99% to 99.6% and nothing was detected
correctly in 2nd
outlier which is AO.
b. Least Absolute Deviation (L1/LAD)
All the method has a low percentage detection in multiple AO and little powerful percentage detection on
multiple IO of 88.3% to 95.3%.In single IO, correct outlier detection percentage for 2
iH , Dffits and iD is 95.7%
to 99.3%. And single AO is 71% to 84.3%.The 1st
outlier which is IO in “both AO and IO” has 95.7% to 98.3%
of correct detection in 2
iH , Dffits and iD . And the 2nd
outlier has approximately 0% all through the methods.
c. Least Median Square (LMS).
2
iH has 96% power of correct outlier detection in single IO and the rest method has power of 79.7% to 83%
while in single AO, all method has power of 69% to 87.3%.For multiple AO, 2
iH has 82.7% to 86% of correct
outlier detection and the rest method has a relatively small percentage of correct detection. 2
iH , Dffits and iD
has a percentage of correct detection of 79.3% to 85% in multiple IO. In “both AO and IO”, 2
iH , Dffits and iD
has a percentage of 96%, 82% and 79.7% on the 1st
outlier which is IO and 0.3% on 2nd
outlier which are IO.
d. Least Trimmed Square (LTS)
2
iH , Dffits and iD has 85.7%, 91.7% and 98% power of correct outlier detection in single IO while in single
AO, all method has less percentage power of 69% to 87.3%.For multiple AO, all the methods has percentage
detection of 43% to 73.7% and for multiple IO 84.7% to 91.3% of correct percentage detection.In “both AO and
IO”, 2
iH , Dffits and iD has a powerful percentage of correct outlier detection of 85.7% to 98% under the 1st
outlier which is IO, and 0.3% on 2nd
outlier which is AO.
Table 3. A simulation study on the power of the outlier detection in regression diagnostics tools
based on ordinary least square (ols)
Summary of the outliers detection performance. Note that the numbers are in percentage.
Table 3. Present the result of the performance of 500 replications on outlier-detection method for each single
and multiple outlier specifications using the cut-off C value of each classical diagnostic techniques as stated
earlier respectively. The numbers and percentage of the correct detection and identification are given under each
location of the outliers. Under multiple outlier and “Both AO and IO”, the label k1, k2, k3 tells us the location
of 1st outlier, 2nd outlier and 3rd
outlier respectively.
26 26 (n=100,ai=0.7,nsimul=500,p=1,k1=26,k2=62,k3=99)
case
Single
AO
Single
IO
3 AOs 3 IOs
Both AO and IO
1st
outlier
2nd
outlier
3rd
outlier
1st
outlier
2nd
outlier
3rd
outlier
1st
outlier
(AO)
2nd
outlier
(IO)
3rd
outlier
(IO)
Ordinary Least Square (OLS)
2
iH 97.6 99.4 88.4 88.8 87.2 95 94.4 93.4 99.4 0.2 0.2
Dffits 67.8 99.4 56 53.8 53.4 98.6 99.2 98.2 99.4 0 0.2
iD 55 99.2 38.6 36.6 35.6 96 96.6 95.6 99.2 0 0.2
6. Identification of Outliersin Time Series Data via...
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The result shows that OLS based diagnostic technique, 2
iH detect 97.6% of correct number of outlier under
Single AO. The percentage detection for single IO under 2
iH , Dffits and iD is very powerful i.e (99.4%, 99.4%
and 99.2%).For multiple IO (3IOs), there seems to be a better correct detection percentage ranging from 93.4%
to 99.2% under the method 2
iH , Dffits and iD . The result outcome of “both AO and IO” seems to favour
additive outliers (AO) and perform woefully under the innovative outlier (IO), this may be that OLS based
diagnostic technique can only detect correctly the first outlier that comes on it way and swamp the rest of the
outlier.
Table 4. A simulation study on the power of the outlier detection based on robust versions of regression
diagnostics tools.
26 26 (n=100,ai=0.7,nsimul=500,p=1,k1=26,k2=62,k3=99)
Case
Single
AO
Single
IO
3 AOs 3 IOs
Both AO and IO
1st
outlie
r
2nd
outlie
r
3rd
Outlie
r
1st
outlie
r
2nd
outlie
r
3rd
outlie
r
1st
outlie
r
(AO)
2nd
outlie
r
(IO)
3rd
outlier
(IO)
M estimation
2
iH 97.6 99.6 88.6 88.8 87 94.6 94.6 94 99.6 0.2 0.2
Dffits 64.8 99.4 52.4 49.8 48.2 99.2 99 98.4 99.4 0.2 0.2
Di 53 99.2 37.6 34.4 34 96.6 95.6 95.6 99.2 0.2% 0.2
L1/Least Absolute Deviation / Least Absolute value
2
iH 98 99.6 90 89.4 87.2 93.8 94 92.8 99.4 0.2 98.4
Dffits 56.4 98.4 49.8 46.2 47 98.8 98.6 98 98.4 0 0.2
iD 47 98.4 36.2 32.2 0.8 96 96 45.8 98.4 0 0.2
Least Median Square
2
iH 97.8 99.4 90.4 90.6 87.8 94.4 94.2 92.2 99.4 0.2 0.2
Dffits 70.4 96.2 55 53 50.6 93.8 92.4 94.2 96.2 0 0.2
iD 56.2 94.6 42.6 38.8 40.8 91 88 88.8 94.6 0 0.2
Least Trimmed Square
2
iH 97.6 99.8 89.2 89.2 87.8 94.6 95 93.8 99.8 0.2 0.2
Dffits 64.6 98.8 53.6 47.2 47.8 98.8 98.4 97.2 98.8 0.2 0.2
iD 49.6 98.4 36.2 33.6 32.8 97.4 94.8 93.8 98.4 0.2 0.2
Summary of the outliers detection performance. Note that the numbers are in percentage.
Table 4. present the results of the proposed methods based on robust version which contains four different kinds
of robust regression namely M-estimation, LAD/L1, LMS and LTS respectively, the table shows the comparison
between their power of performance accordingly in the correctly detection and identification in percentage.
Which one has the most powerful performance among the robust regressions, however their outliers’ locations
and cut-off values C are set as the same in Table 1. The interpretations of the table are as follows:
7. Identification of Outliersin Time Series Data via...
DOI: 10.9790/5728-11656067 www.iosrjournals.org 66 | Page
a. M-estimation
The power of correct detection and identification percentage for multiple and single AO is very poor under the
M-estimation except for 2
iH single AO that has 99.6% correct detection.Under multiple IO, the power of
correct percentage detection and identification is between 94% to 99.6% for 2
iH , Dffits and iD which is quiet
powerful. 2
iH , Dffits and iD has 99.2% to 99.6% power of correctly detection and identification under the 1st
outlier which is AO in “Both AO and IO” and nothing was detected correctly in 2nd
outlier and 3rd
outlier which
were IOs.
b. Least Absolute Deviation (L1/LAD)
Dffits and iD has 0.8% to 56.4% power of correct outlier detection which is relatively low, meanwhile
2
iH has
correct power detection of 87.2% to 90% in multiple AO and 98% in single AO.In single and multiple IO,
correct outlier detection percentage for 2
iH , Dffits and iD which has the percentage of 92.8% to 99.6%.The 1st
outlier which is AO in “both AO and IO” has 98.4% to 99.4% of correct detection in 2
iH , Dffits and iD . For 2nd
and 3rd
outlier, they have approximately 0% correct detection expect the 2
iH 3rd
outlier that has 98.4% correct
detection.
c. Least Median Square (LMS).
Only 2
iH attain 97.8% correct percentage detection in single AO and 87.8% to 90.6% correct detection from the
1st
outlier to 3rd
outlier in multiple AO. 2
iH also have 87.8% to 90.6% in multiple IO and 99.4% in single IO,
however Dffits and iD has correct percentage detection of 96.2% and 94.6% in single IO and 88% to 94.4% in
multiple IO.In “both AO and IO”, 2
iH , Dffits and iD has a powerful percentage of 99.4%,96.2% and 94.6% on
the 1st
outlier which is AO and 0% to 0.2% on 2nd
and 3rd
outlier which are IO.
d. Least Trimmed Square (LTS)
2
iH has 97.6% power of correct outlier detection in single AO and the rest method has power of 1% to 64.6%
while in single IO, all method has power of 98.4% to 99.8%.For multiple AO, 2
iH has 87.8% to 89.2% of
correct outlier detection and the rest method has a relatively small percentage of correct detection. 2
iH , Dffits
and iD has a percentage of correct detection of 93.8% to 98.8% in multiple IO.In “both AO and IO”, 2
iH ,
Dffits and iD has a powerful percentage of correct outlier detection of 98.4% to 99.8% under the 1st
outlier
which is AO, and 0.2% on 2nd
and 3rd
outlier which are IO.
IV. SUMMARY
It is seen from the result that under small sample size, OLS performance is good under innovative outlier for
single, and multiple outliers, and also in “both AO and IO”, the percentage correction outlier detection is only
innovative outlier. However, generally, the power of percentage correct outlier detection only give best result
for both OLS based and robust version based under innovative outliers. Robust version base on M-estimation
and OLS based perform similar way and the rest robust version method perform less.
Under large sample size, the result also indicate that regression diagnostics tools based on OLS perform similar
way to other various kind of robust versions that are based on robust regression. However, in this part of the
power of correct outlier detection, LTS performance is the best with the probability percentage of 87.8% to
99.8% followed by L1/LAD (87.2% to 99.6%), LMS (87.8% to 99.4%) and M-estimation (87% to 99.6%). In
spite of this Hadi’s influence measure 𝐻𝑖
2
perform the best in both OLS based and robust based version.
Meanwhile, all diagnostics measure didn’t detect any outlier in “both AO and IO” except for the first outlier
which is AO, no outlier is detected in second and third outlier under IO.
V. Conclusion
In a small sample size, OLS and M-estimation is suggest to be use for the detection and identification of outlier
under innovative outliers (IO). However both method fail to detect any number of correct outliers detection
when the sample were mixed by both innovative outliers and additive outliers. Other robust estimation methods
8. Identification of Outliersin Time Series Data via...
DOI: 10.9790/5728-11656067 www.iosrjournals.org 67 | Page
performed less. On the other side, large sample size, LTS perform best in the simulation study compared to
other measures, however it is not robust to a series that is contaminated with both AO and IO. It can only detect
the AO in the series. Also Cook’s Distance and The welsch-kuh distance are not robust to multiple AO.
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