This document discusses different statistical modeling approaches for pricing motor third party liability insurance. It begins by introducing the theoretical framework for pricing risk premiums based on expected claim frequency and severity. It then describes moving from a technical tariff to a commercial tariff by adjusting for safety and loading rates. The rest of the document applies generalized linear models (GLM), generalized non-linear models (GNM), and generalized additive models (GAM) to an Australian private motor insurance dataset to model stochastic risk premiums. It compares the results of the different modeling approaches based on metrics like the mean commercial tariff, loss ratio, explained deviance, and number of risk coefficients.
This talk was part of a joint KLM-BigData Republic data science meetup to share results and learnings of a full-cycle data science project on passenger forecasting. I presented the data science part of the project, including how to frame the modeling problem, performance metrics, validation strategy, machine-learning algorithms and challenges from a data science perspective.
Forecasting airline passengers with designer machine learningAlexander Backus
The ability to accurately forecast the amount of passengers that will board a particular flight is crucial for airline operations. But how do we design a machine learning algorithm for this use case and in what ways can we improve it? In this talk, we start with a simple linear model, evolving to increasingly complex deep learning neural network architectures.
This talk was part of a joint KLM-BigData Republic data science meetup to share results and learnings of a full-cycle data science project on passenger forecasting. I presented the data science part of the project, including how to frame the modeling problem, performance metrics, validation strategy, machine-learning algorithms and challenges from a data science perspective.
Forecasting airline passengers with designer machine learningAlexander Backus
The ability to accurately forecast the amount of passengers that will board a particular flight is crucial for airline operations. But how do we design a machine learning algorithm for this use case and in what ways can we improve it? In this talk, we start with a simple linear model, evolving to increasingly complex deep learning neural network architectures.
This presentation educates you about R - Logistic Regression,
and glm() function with description parameters including sample example, Input Data for glm() function, Create Regression Model.
For more topics stay tuned with Learnbay.
This presentation educates you about R - Logistic Regression,
and glm() function with description parameters including sample example, Input Data for glm() function, Create Regression Model.
For more topics stay tuned with Learnbay.
Using portfolio diversification and risk modeling techniques determine if Insurance portfolio is less volatile than Tech portfolio.
Covers below :
Risk Modeling
Portfolio Diversification
Time Series Forecasting
ARIMA + GARCH + Copula
COMBINED ECONOMIC AND EMISSION DISPATCH WITH AND WITHOUT CONSIDERING TRANSMIS...cscpconf
This paper gives a complete idea about the Combined Economic and Emission Dispatch(CEED) in different load demand. This paper shows CEED of a six generator system by
neglecting the transmission loss first, and after that CEED of the same system considering transmission loss. Here we solve the CEED problem with the help of Mat-Lab software. The
results are graphically represented here, like generation cost v/s load demands; load shared byeach generator in different load demand and transmission loss v/s load demands.
Consider a 4-Link robot manipulator shown below. Use the forward kine.pdfmeerobertsonheyde608
Consider a 4-Link robot manipulator shown below. Use the forward kinematic D-H table and
write an m file that plots the manipulator. The instructions are given in the module 6. Submit
your solutions by the due date, in a single MATLAB m file.
Solution
Please give the kinetic D-H table else it would be difficult to code as we need to know the
rotation spin axis and other momentum of manipulator
Stating a general example code for manipulator with data
function X = fwd_kin(q,x)
% given a position in the configuration space, calculate the position of
% the end effector in the workspace for a two-link manipulator.
% q: vector of joint positions
% x: design vector (link lengths)
% X: end effector position in cartesian coordinates
% configuration space coordinates:
q1 = q(1); % theta 1
q2 = q(2); % theta 2
% manipulator parameters:
l1 = x(1); % link 1 length
l2 = x(2); % link 2 length
% calculate end effector position:
X = [l1*cos(q1) + l2*cos(q1+q2)
l1*sin(q1) + l2*sin(q1+q2)];
% SimulateTwolink.m uses inverse dynamics to simulate the torque
% trajectories required for a two-link planar robotic manipulator to follow
% a prescribed trajectory. It also computes total energy consumption. This
% code is provided as supplementary material for the paper:
%
% \'Engineering System Co-Design with Limited Plant Redesign\'
% Presented at the 8th AIAA Multidisciplinary Design Optimization
% Specialist Conference, April 2012.
%
% The paper is available from:
%
% http://systemdesign.illinois.edu/publications/All12a.pdf
%
% Here both the physical system design and control system design are
% considered simultaneously. Manipulator link length and trajectory
% specification can be specified, and torque trajectory and energy
% consumption are computed based on this input. It was found that maximum
% torque and total energy consumption calculated using inverse dynamics
% agreed closely with results calculated using feedback linearization, so
% to simplify optimization problem solution an inverse dynamics approach
% was used, which reduces the control design vector to just the trajectory
% design.
%
% In the conference paper several cases are considered, each with its own
% manipulator task, manipulator design, and trajectory design. The
% specifications for each of these five cases are provided here, and can be
% explored by changing the case number variable (cn).
%
% This code was incorporated into a larger optimization project. The code
% presented here includes only the analysis portion of the code, no
% optimization.
%
% A video illustrating the motion of each of these five cases is available
% on YouTube:
%
% http://www.youtube.com/watch?v=OR7Y9-n5SjA
%
% Author: James T. Allison, Assistant Professor, University of Illinois at
% Urbana-Champaign
% Date: 4/10/12
clear;clc
% simulation parameters:
p.dt = 0.0005; % simulation step size
tf = 2; p.tf = tf; % final time
p.ploton = 0; % turn off additional plotting capabilities
p.ploton2 = 0;
p.Tallow = 210; % maximum .
Automated Sensing System for Monitoring Road Surface Condition Using Fog Comp...IJAEMSJORNAL
The principle point of this task is to build up an Intelligent Monitoring System used to screen the Road Surface Condition using Fog Computing that increases the road safety. Multiple solutions have been proposed which make use of mobile sensing, more specifically contemporary applications and architectures that are used in both crowd sensing and vehicle based sensing. Nonetheless, these initiatives have not been without challenges that range from mobility support, location awareness, low latency as well as geo-distribution. As a result, a new term has been coined for this novel paradigm, called, fog computing.
Learn the built-in mathematical functions in R. This tutorial is part of the Working With Data module of the R Programming course offered by r-squared.
Simulators play a major role in analyzing multi-modal transportation networks. As their complexity increases, optimization becomes an increasingly challenging task. Current calibration procedures often rely on heuristics, rules of thumb and sometimes on brute-force search. Alternatively, we provide a statistical method which combines a distributed, Gaussian Process Bayesian optimization method with dimensionality reduction techniques and structural improvement. We then demonstrate our framework on the problem of calibrating a multi-modal transportation network of city of Bloomington, Illinois. Our framework is sample efficient and supported by theoretical analysis and an empirical study. We demonstrate on the problem of calibrating a multi-modal transportation network of city of Bloomington, Illinois. Finally, we discuss directions for further research.
There are now a couple of alternative interpreters (or engines) for the R programming language. In this presentation, I will give a gentle introduction to Renjin, which is an open-source project to implement an R interpreter in Java.The introduction should appeal a wide audience, from data scientists to (web) application developers and will cover topis such as "Why build another R interpreter?", "For whom is Renjin?", "What can you do with Renjin?", "How does Renjin compare to GNU R and the other alternative engines like pqR, FastR, and TERR or pseudo-alternatives like Microsoft R and Oracle R Distribution?", "How can I try Renjin?", and more.
Where South America is Swinging to the Right: An R-Driven Data Journalism Pr...Zurich_R_User_Group
Where South America is Swinging to the Right: An R-Driven Data Journalism Project from Neue Zürcher Zeitung - Marie-José Kolly @mjKolly, Marvin Milatz@marvinmilatz
Screening data is still a laborious task in R. Calculating summary statistics for all variables while listing the occurrence of missing data and producing some kind of graphics is a three-click process in SPSS, but base R does not contain higher level functions for quickly describing bigger datasets in a more or less automated way. The R package DescTools addresses three problem areas. First it provides functions meant to facilitate the construction of univariate and bivariate descriptive tables of several variable types. Then the connectivity between R and MS-Office is enhanced by providing an easy interface to Word and Excel. Generating reports directly in Word and importing data directly from Excel becomes an easy task. Finally a considerable amount of base functions (operators, string and date functions, statistics, tests, several plot types) not present in base R is collected from other packages and internet sources with the goal to have them consolidated in ONE instead of dozens of packages and to have a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned.
January 2016 Meetup: Speeding up (big) data manipulation with data.table packageZurich_R_User_Group
Abstract: Both practitioners and researchers spend significant amount of their time on data preparation, cleaning and exploration. It gets more complicated and interesting if a dataset is big, or if it has a lot of groups in it which require per-group analysis. In this talk I will introduce an innovative data.table package as an alternative to the standard data.frame which significantly cuts your programming and execution time with easier code. It is also the first step to working with big data in R. The talk will be beneficial for R users from all disciplines, as well as for big data professionals looking for more explicit data exploration tools.
December 2015 Meetup - Shiny: Make Your R Code Interactive - Craig WangZurich_R_User_Group
Do you want to create R functions that can be accessed by non-R users? Do you want your R code to be interactive and alive? Shiny is a freely-available R package that provides a programming environment to turn your R analyses into interactive web applications. In this presentation, I will introduce the Shiny package and its basic structure. It enables our R code to dynamically react to user input, and it is straight-forward to implement without any prior knowledge in website design. I will also demonstrate some examples of web applications designed using Shiny.
Utilocate offers a comprehensive solution for locate ticket management by automating and streamlining the entire process. By integrating with Geospatial Information Systems (GIS), it provides accurate mapping and visualization of utility locations, enhancing decision-making and reducing the risk of errors. The system's advanced data analytics tools help identify trends, predict potential issues, and optimize resource allocation, making the locate ticket management process smarter and more efficient. Additionally, automated ticket management ensures consistency and reduces human error, while real-time notifications keep all relevant personnel informed and ready to respond promptly.
The system's ability to streamline workflows and automate ticket routing significantly reduces the time taken to process each ticket, making the process faster and more efficient. Mobile access allows field technicians to update ticket information on the go, ensuring that the latest information is always available and accelerating the locate process. Overall, Utilocate not only enhances the efficiency and accuracy of locate ticket management but also improves safety by minimizing the risk of utility damage through precise and timely locates.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
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(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Mobile App Development Company In Noida | Drona InfotechDrona Infotech
Looking for a reliable mobile app development company in Noida? Look no further than Drona Infotech. We specialize in creating customized apps for your business needs.
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OpenMetadata Community Meeting - 5th June 2024OpenMetadata
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
E-commerce Application Development Company.pdfHornet Dynamics
Your business can reach new heights with our assistance as we design solutions that are specifically appropriate for your goals and vision. Our eCommerce application solutions can digitally coordinate all retail operations processes to meet the demands of the marketplace while maintaining business continuity.
Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
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See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
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Understanding Nidhi Software Pricing: A Quick Guide 🌟
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2. Motor Third Party Liability
Pricing
By the Insurance contract, economic risk is transferred
from the policyholder to the Insurer
3. Theoretical Approach
P=E(X)=E(N)*E(Z)
P=Risk Premium
X=Global Loss
E(N)=claim frequency
E(Z)=claim severity
Hp:
1) cost of claims are i.i.d.
2) indipendence between number of claims
and cost of claims
4. From Technical Tariff to
Commercial Tariff
Tariff variables
P=Pcoll*Yh*Xi*Zj=Technical Tariff
risk coefficients statistical models are employed
Pt=P*(1+λ)/(1-H)=Commercial Tariff
λ=Safety Loading Rate
H=Loading Rate
P is adjusted by tariff requirement
6. > table(VehAge,useNA="always")
VehAge
old cars oldest cars young cars youngest cars <NA>
20064 18948 16587 12257 0
> table(DrivAge,useNA="always")
DrivAge
old people older work. people oldest people working people
10736 16189 6547 15767
young people youngest people <NA>
12875 5742 0
> table(VehBody,useNA="always")
VehBody
Bus Convertible Coupe Hardtop
48 81 780 1579
Hatchback Minibus Motorized caravan Panel van
18915 717 127 752
Roadster Sedan Station wagon Truck
27 22233 16261 1750
Utility <NA>
4586 0
12. Cluster Analysis by k-means
#Prepare Data
> rc.stand<-scale(rc[-1]) # To standardize the variables
#Determine number of clusters
> nk = 2:10
> WSS = sapply(nk, function(k) {
+ kmeans(rc.stand, centers=k)$tot.withinss
+ })
> plot(nk, WSS, type="l", xlab="Number of Clusters",
+ ylab="Within groups sum of squares")
#k-means with k = 7 solutions
> k.means.fit <- kmeans(rc.stand, 7)
13. 2 4 6 8 10
6000080000100000120000140000
Number of Clusters
Withingroupssumofsquares
14. Generalized Linear Models
(GLM)Yi~EF(b(θi);Φ/ωi) g(μi)=ηi ηi=Σjxijβj
Random Component Link Systematic Component
Linear Models are extended in
two directions:
Probability distribution:
Output variables are stochastically
independent with the same exponential
family distribution.
Expected value:
There is a link function between
expected value of outputs and covariates
that could be different from linear
regression.
16. Generalized NonLinear
Models (GNM)
Yi~EF(b(θi);Φ/ωi) g(μi)=ηi(xij;βj) ηi=Σjxijβj
Random Component Link Systematic Component
Generalized Linear Models are extended
in the link function where the
systematic component is non linear
in the parameters βj.
It can be considered an extension of
nonlinear least squares model, where the
variance of the output depend on the mean.
Difficult are in starting values, they are
generated randomly for non linear
parameters and using a GLM fit for linear
parameters.
18. Generalized Additive
Models (GAM)
Yi~EF(b(θi);Φ/ωi) g(μi)=ηi ηi= Σpxipβip+Σjfj(xij)
Random Component Link Systematic Component
Generalized additive models extend
generalized linear models in the predictor:
systematic component is made up by one
parametric part and one non parametric part
built by the sum of unknown “smoothing”
functions of the covariates.
For the estimators are used splines,
functions made up by combination of
little polynomial segment joined in knots.
21. GLM vs GAM vs GNM
Approaches
GLM GAM GNM
Strengths: -User-friendly -Flexible to fit data -Afford some
-Faster elaboration -Realistic values elaboration
-Usually low level of excluded by GLM
residual deviance
-More risk coefficients -Better values
despite GLM
Weakness: -Poor flexibility -Complex to realize -Complex to use
to fit data
-Usually higher
values of residual
deviance
-Overestimed values
22. References
C.G. Giancaterino - GLM, GNM and GAM Approaches on MTPL Pricing -
Journal of Mathematics and Statistical Science – 08/2016
http://www.ss-pub.org/journals/jmss/vol-2/vol-2-issue-8-august-2016/
X.Marechal & S. Mahy – Advanced Non Life Pricing – EAA Seminar
N. Savelli & G.P. Clemente – Lezioni di Matematica Attuariale delle
Assicurazioni Danni – Educatt
23. Many Thanks for your Attention!!!
Contact:
Claudio G. Giancaterino
c.giancaterino@gmail.com