This document summarizes several methods for estimating copula densities from sample data in a nonparametric way, including using kernel density estimation with different types of kernels and variable transformations. It describes the standard kernel estimate, issues with it near boundaries, a mirror kernel estimate, using beta kernels, a probit transformation of variables, and improved probit transformation estimators that use local polynomial approximations. The goal is to find estimators that are consistent along the boundaries of the copula support and improve inference about the copula density.
It covers knowledge representation techniques using propositional and predicate logic. It also discusses about the knowledge inference using resolution refutation process, rule based system and bayesian network.
Fixed Point Results In Fuzzy Menger Space With Common Property (E.A.)IJERA Editor
This paper presents some common fixed point theorems for weakly compatible mappings via an implicit relation in Fuzzy Menger spaces satisfying the common property (E.A)
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
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Professor Timoteo Carletti presented a seminar titled "A journey in the zoo of Turing patterns: the topology does matter as part of the SMART Seminar Series on 8th March 2018.
More information: http://www.uoweis.co/event/a-journey-in-the-zoo-of-turing-patterns-the-topology-does-matter/
Keep updated with future events: http://www.uoweis.co/events/category/smart-infrastructure-facility/
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
A pyramid is a structure whose outer surfaces are triangular and converge to a single step at the top, making the shape roughly a pyramid in the geometric sense. The base of a pyramid can be trilateral, quadrilateral, or of any polygon shape. As such, a pyramid has at least three outer triangular surfaces. Wikipedia
पिरामिड जैसे ज्यामितीय आकार से मिलती जुलती संरचनाओं को पिरामिड कहते हैं। विश्व में बहुत सी संरचनाएँ पिरैमिड के आकार की हैं जिनमें मिस्र के पिरामिड बहुत प्रसिद्ध हैं। पिरामिड आकार की संरचनाओं की सबसे बड़ी विशेषता यह है कि इसके भार का अधिकांश भाग जमीन के पास होता है
Similar to transformations and nonparametric inference (20)
Talk at the modcov19 CNRS workshop, en France, to present our article COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
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Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
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Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
transformations and nonparametric inference
1. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Using Transformations of Variables to Improve Inference
A. Charpentier ( UQAM),
joint work with J.-D. Fermanian (CREST) E. Flachaire (AMSE) G. Geenens
(UNSW), A. Oulidi (UIO), D. Paindaveine (ULB), O. Scaillet (UNIGE)
Montréal, UQAM, November 2018
@freakonometrics freakonometrics freakonometrics.hypotheses.org 1
2. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Side Note
Most of the contents here is based on old results (revised, and continued)
Work on genealogical trees, Étude de la démographie française du XIXe siècle à
partir de données collaboratives de généalogie and Internal Migrations in France in
the Nineteenth Century with E. Gallic.
Children Grandchildren Great-grandchildren
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Children Grandchildren Great-grandchildren
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0.058
0.087
0.116
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3. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Motivation
2005, The Estimation of Copulas: Theory and Practice
with J.-D. Fermanian and O. Scaillet
survey on non-parametric techniques
(kernel base) to visualize
the estimator of a copula density
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Idea : beta kernels and transformed kernels
More recently, mix those techniques in the univariate case
@freakonometrics freakonometrics freakonometrics.hypotheses.org 3
4. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Motivation
Consider some n-i.i.d. sample {(Xi, Yi)} with cu-
mulative distribution function FXY and joint den-
sity fXY . Let FX and FY denote the marginal dis-
tributions, and C the copula,
FXY (x, y) = C(FX(x), FY (y))
so that
fXY (x, y) = fX(x)fY (y)c(FX(x), FY (y))
We want a nonparametric estimate of c on [0, 1]2
. 1e+01 1e+03 1e+05
1e+011e+021e+031e+041e+05
@freakonometrics freakonometrics freakonometrics.hypotheses.org 4
5. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Notations
Define uniformized n-i.i.d. sample {(Ui, Vi)}
Ui = FX(Xi) and Vi = FY (Yi)
or uniformized n-i.i.d. pseudo-sample {( ˆUi, ˆVi)}
ˆUi =
n
n + 1
ˆFXn(Xi) and ˆVi =
n
n + 1
ˆFY n(Yi)
where ˆFXn and ˆFY n denote empirical c.d.f.
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0.00.20.40.60.81.0
@freakonometrics freakonometrics freakonometrics.hypotheses.org 5
6. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Standard Kernel Estimate
The standard kernel estimator for c, say ˆc∗
, at (u, v) ∈ I would be (see Wand &
Jones (1995))
ˆc∗
(u, v) =
1
n|HUV |1/2
n
i=1
K H
−1/2
UV
u − Ui
v − Vi
, (1)
where K : R2
→ R is a kernel function and HUV is a bandwidth matrix.
@freakonometrics freakonometrics freakonometrics.hypotheses.org 6
7. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Standard Kernel Estimate
However, this estimator is not consistent along
boundaries of [0, 1]2
E(ˆc∗
(u, v)) =
1
4
c(u, v) + O(h) at corners
E(ˆc∗
(u, v)) =
1
2
c(u, v) + O(h) on the borders
if K is symmetric and HUV symmetric.
Corrections have been proposed, e.g. mirror reflec-
tion Gijbels (1990) or the usage of boundary kernels
Chen (2007), but with mixed results.
Remark : the graph on the bottom is ˆc∗
on the
(first) diagonal. 0.0 0.2 0.4 0.6 0.8 1.0
01234567
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8. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Mirror Kernel Estimate
Use an enlarged sample : instead of only {( ˆUi, ˆVi)},
add {(− ˆUi, ˆVi)}, {( ˆUi, − ˆVi)}, {(− ˆUi, − ˆVi)},
{( ˆUi, 2 − ˆVi)}, {(2 − ˆUi, ˆVi)},{(− ˆUi, 2 − ˆVi)},
{(2 − ˆUi, − ˆVi)} and {(2 − ˆUi, 2 − ˆVi)}.
See Gijbels & Mielniczuk (1990).
That estimator will be used as a benchmark in the
simulation study.
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9. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Using Beta Kernels
Use a Kernel which is a product of beta kernels
Kxi
(u) ∝ x
u1
b
1,i [1 − x1,i]
u1
b · x
u2
b
2,i [1 − x2,i]
u2
b
for some b > 0, see Chen (1999).
for some observation xi in the lower left corner
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10. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Using Beta Kernels
@freakonometrics freakonometrics freakonometrics.hypotheses.org 10
11. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Probit Transformation
See Devroye & Gyöfi (1985) and Marron & Ruppert
(1994).
Define normalized n-i.i.d. sample {(Si, Ti)}
Si = Φ−1
(Ui) and Ti = Φ−1
(Vi)
or normalized n-i.i.d. pseudo-sample {( ˆSi, ˆTi)}
ˆUi = Φ−1
( ˆUi) and ˆVi = Φ−1
( ˆVi)
where Φ−1
is the quantile function of N(0, 1)
(probit transformation). −3 −2 −1 0 1 2 3
−3−2−10123
@freakonometrics freakonometrics freakonometrics.hypotheses.org 11
12. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Probit Transformation
FST (x, y) = C(Φ(x), Φ(y))
so that
fST (x, y) = φ(x)φ(y)c(Φ(x), Φ(y))
Thus
c(u, v) =
fST (Φ−1
(u), Φ−1
(v))
φ(Φ−1(u))φ(Φ−1(v))
.
So use
ˆc(τ)
(u, v) =
ˆfST (Φ−1
(u), Φ−1
(v))
φ(Φ−1(u))φ(Φ−1(v))
−3 −2 −1 0 1 2 3
−3−2−10123
@freakonometrics freakonometrics freakonometrics.hypotheses.org 12
13. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
The naive estimator
Since we cannot use
ˆf∗
ST (s, t) =
1
n|HST |1/2
n
i=1
K H
−1/2
ST
s − Si
t − Ti
,
where K is a kernel function and HST is a band-
width matrix, use
ˆfST (s, t) =
1
n|HST |1/2
n
i=1
K H
−1/2
ST
s − ˆSi
t − ˆTi
.
and the copula density is
ˆc(τ)
(u, v) =
ˆfST (Φ−1
(u), Φ−1
(v))
φ(Φ−1(u))φ(Φ−1(v))
0.0 0.2 0.4 0.6 0.8 1.0
01234567
@freakonometrics freakonometrics freakonometrics.hypotheses.org 13
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14. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
The naive estimator
ˆc(τ)
(u, v) =
1
n|HST |1/2φ(Φ−1(u))φ(Φ−1(v))
n
i=1
K H
−1/2
ST
Φ−1
(u) − Φ−1
( ˆUi)
Φ−1(v) − Φ−1( ˆVi)
as suggested in C., Fermanian & Scaillet (2007) and Lopez-Paz . et al. (2013).
Note that Omelka . et al. (2009) obtained theoretical properties on the
convergence of ˆC(τ)
(u, v) (not c).
In Probit transformation for nonparametric kernel estimation of the copula density
with G. Geenens and D. Paindaveine, we extended that estimator.
See also kdecopula R package by T. Nagler
@freakonometrics freakonometrics freakonometrics.hypotheses.org 14
15. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Improved probit-transformation copula density estimators
When estimating a density from pseudo-sample, Loader (1996) and Hjort &
Jones (1996) define a local likelihood estimator
Around (s, t) ∈ R2
, use a polynomial approximation of order p for log fST
log fST (ˇs, ˇt) a1,0(s, t) + a1,1(s, t)(ˇs − s) + a1,2(s, t)(ˇt − t)
.
= Pa1
(ˇs − s, ˇt − t)
log fST (ˇs, ˇt) a2,0(s, t) + a2,1(s, t)(ˇs − s) + a2,2(s, t)(ˇt − t)
+ a2,3(s, t)(ˇs − s)2
+ a2,4(s, t)(ˇt − t)2
+ a2,5(s, t)(ˇs − s)(ˇt − t)
.
= Pa2
(ˇs − s, ˇt − t).
@freakonometrics freakonometrics freakonometrics.hypotheses.org 15
16. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Improved probit-transformation copula density estimators
Remark Vectors a1(s, t) = (a1,0(s, t), a1,1(s, t), a1,2(s, t)) and
a2(s, t)
.
= (a2,0(s, t), . . . , a2,5(s, t)) are then estimated by solving a weighted
maximum likelihood problem.
˜ap(s, t) = arg max
ap
n
i=1
K H
−1/2
ST
s − ˆSi
t − ˆTi
Pap
( ˆSi − s, ˆTi − t)
−n
R2
K H
−1/2
ST
s − ˇs
t − ˇt
exp Pap (ˇs − s, ˇt − t) dˇs dˇt ,
The estimate of fST at (s, t) is then ˜f
(p)
ST (s, t) = exp(˜ap,0(s, t)), for p = 1, 2.
The Improved probit-transformation kernel copula density estimators are
˜c(τ,p)
(u, v) =
˜f
(p)
ST (Φ−1
(u), Φ−1
(v))
φ(Φ−1(u))φ(Φ−1(v))
@freakonometrics freakonometrics freakonometrics.hypotheses.org 16
17. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Improved probit-transformation
copula density estimators
For the local log-linear (p = 1) approximation
˜c(τ,1)
(u, v) =
exp(˜a1,0(Φ−1
(u), Φ−1
(v))
φ(Φ−1(u))φ(Φ−1(v))
0.0 0.2 0.4 0.6 0.8 1.0
01234567
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18. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Improved probit-transformation
copula density estimators
For the local log-quadratic (p = 2) approximation
˜c(τ,2)
(u, v) =
exp(˜a2,0(Φ−1
(u), Φ−1
(v))
φ(Φ−1(u))φ(Φ−1(v))
0.0 0.2 0.4 0.6 0.8 1.0
01234567
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19. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Asymptotic properties
A1. The sample {(Xi, Yi)} is a n- i.i.d. sample from the joint distribution FXY ,
an absolutely continuous distribution with marginals FX and FY strictly
increasing on their support ;
(uniqueness of the copula)
@freakonometrics freakonometrics freakonometrics.hypotheses.org 19
20. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Asymptotic properties
A2. The copula C of FXY is such that (∂C/∂u)(u, v) and (∂2
C/∂u2
)(u, v) exist
and are continuous on {(u, v) : u ∈ (0, 1), v ∈ [0, 1]}, and (∂C/∂v)(u, v) and
(∂2
C/∂v2
)(u, v) exist and are continuous on {(u, v) : u ∈ [0, 1], v ∈ (0, 1)}. In
addition, there are constants K1 and K2 such that
∂2
C
∂u2
(u, v) ≤
K1
u(1 − u)
for (u, v) ∈ (0, 1) × [0, 1];
∂2
C
∂v2
(u, v) ≤
K2
v(1 − v)
for (u, v) ∈ [0, 1] × (0, 1);
A3. The density c of C exists, is positive and admits continuous second-order
partial derivatives on the interior of the unit square I. In addition, there is a
constant K00 such that
c(u, v) ≤ K00 min
1
u(1 − u)
,
1
v(1 − v)
∀(u, v) ∈ (0, 1)2
.
see Segers (2012).
@freakonometrics freakonometrics freakonometrics.hypotheses.org 20
21. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Asymptotic properties
Assume that K(z1, z2) = φ(z1)φ(z2) and HST = h2
I with h ∼ n−a
for some
a ∈ (0, 1/4). Under Assumptions A1-A3, the ‘naive’ probit transformation kernel
copula density estimator at any (u, v) ∈ (0, 1)2
is such that
√
nh2 ˆc(τ)
(u, v) − c(u, v) − h2 b(u, v)
φ(Φ−1(u))φ(Φ−1(v))
L
−→ N 0, σ2
(u, v) ,
where b(u, v) =
1
2
∂2
c
∂u2
(u, v)φ2
(Φ−1
(u)) +
∂2
c
∂v2
(u, v)φ2
(Φ−1
(v))
− 3
∂c
∂u
(u, v)Φ−1
(u)φ(Φ−1
(u)) +
∂c
∂v
(u, v)Φ−1
(v)φ(Φ−1
(v))
+ c(u, v) {Φ−1
(u)}2
+ {Φ−1
(v)}2
− 2 (2)
and σ2
(u, v) =
c(u, v)
4πφ(Φ−1(u))φ(Φ−1(v))
.
@freakonometrics freakonometrics freakonometrics.hypotheses.org 21
22. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
The Amended version
The last unbounded term in b be easily adjusted.
ˆc(τam)
(u, v) =
ˆfST (Φ−1
(u), Φ−1
(v))
φ(Φ−1(u))φ(Φ−1(v))
×
1
1 + 1
2 h2 ({Φ−1(u)}2 + {Φ−1(v)}2 − 2)
.
The asymptotic bias becomes proportional to
b(am)
(u, v) =
1
2
∂2
c
∂u2
(u, v)φ2
(Φ−1
(u)) +
∂2
c
∂v2
(u, v)φ2
(Φ−1
(v))
−3
∂c
∂u
(u, v)Φ−1
(u)φ(Φ−1
(u)) +
∂c
∂v
(u, v)Φ−1
(v)φ(Φ−1
(v)) .
@freakonometrics freakonometrics freakonometrics.hypotheses.org 22
23. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
A local log-linear probit-transformation kernel estimator
˜c∗(τ,1)
(u, v) = ˜f
∗(1)
ST (Φ−1
(u), Φ−1
(v))/ φ(Φ−1
(u))φ(Φ−1
(v))
Then
√
nh2 ˜c∗(τ,1)
(u, v) − c(u, v) − h2 b(1)
(u, v)
φ(Φ−1(u))φ(Φ−1(v))
L
−→ N 0, σ(1) 2
(u, v) ,
where b(1)
(u, v) =
1
2
∂2
c
∂u2
(u, v)φ2
(Φ−1
(u)) +
∂2
c
∂v2
(u, v)φ2
(Φ−1
(v))
−
1
c(u, v)
∂c
∂u
(u, v)
2
φ2
(Φ−1
(u)) +
∂c
∂v
(u, v)
2
φ2
(Φ−1
(v))
−
∂c
∂u
(u, v)Φ−1
(u)φ(Φ−1
(u)) +
∂c
∂v
(u, v)Φ−1
(v)φ(Φ−1
(v)) − 2c(u, v)
@freakonometrics freakonometrics freakonometrics.hypotheses.org 23
24. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Using a higher order polynomial approximation
Locally fitting a polynomial of a higher degree is known to reduce the asymptotic
bias of the estimator, here from order O(h2
) to order O(h4
), see Loader (1996) or
Hjort (1996), under sufficient smoothness conditions.
If fST admits continuous fourth-order partial derivatives and is positive at (s, t),
then
√
nh2 ˜f
∗(2)
ST (s, t) − fST (s, t) − h4
b
(2)
ST (s, t)
L
−→ N 0, σ
(2)
ST
2
(s, t) ,
where σ
(2)
ST
2
(s, t) =
5
2
fST (s, t)
4π
and
b
(2)
ST (s, t) = −
1
8
fST (s, t)
×
∂4
g
∂s4
+
∂4
g
∂t4
+ 4
∂3
g
∂s3
∂g
∂s
+
∂3
g
∂t3
∂g
∂t
+
∂3
g
∂s2∂t
∂g
∂t
+
∂3
g
∂s∂t2
∂g
∂s
+ 2
∂4
g
∂s2∂t2
(s, t),
with g(s, t) = log fST (s, t).
@freakonometrics freakonometrics freakonometrics.hypotheses.org 24
25. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Using a higher order polynomial approximation
A4. The copula density c(u, v) = (∂2
C/∂u∂v)(u, v) admits continuous
fourth-order partial derivatives on the interior of the unit square [0, 1]2
.
Then
√
nh2 ˜c∗(τ,2)
(u, v) − c(u, v) − h4 b(2)
(u, v)
φ(Φ−1(u))φ(Φ−1(v))
L
−→ N 0, σ(2) 2
(u, v)
where σ(2) 2
(u, v) =
5
2
c(u, v)
4πφ(Φ−1(u))φ(Φ−1(v))
@freakonometrics freakonometrics freakonometrics.hypotheses.org 25
26. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Improving Bandwidth choice
Consider the principal components decomposition of the (n × 2) matrix
[ˆS, ˆT ] = M.
Let W1 = (W11, W12)T
and W2 = (W21, W22)T
be the eigenvectors of MT
M. Set
Q
R
=
W11 W12
W21 W22
S
T
= W
S
T
which is only a linear reparametrization of R2
, so
an estimate of fST can be readily obtained from an
estimate of the density of (Q, R)
Since { ˆQi} and { ˆRi} are empirically uncorrela-
ted, consider a diagonal bandwidth matrix HQR =
diag(h2
Q, h2
R).
−4 −2 0 2 4
−3−2−1012
@freakonometrics freakonometrics freakonometrics.hypotheses.org 26
27. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Improving Bandwidth choice
Use univariate procedures to select hQ and hR independently
Denote ˜f
(p)
Q and ˜f
(p)
R (p = 1, 2), the local log-polynomial estimators for the
densities
hQ can be selected via cross-validation (see Section 5.3.3 in Loader (1999))
hQ = arg min
h>0
∞
−∞
˜f
(p)
Q (q)
2
dq −
2
n
n
i=1
˜f
(p)
Q(−i)( ˆQi) ,
where ˜f
(p)
Q(−i) is the ‘leave-one-out’ version of ˜f
(p)
Q .
@freakonometrics freakonometrics freakonometrics.hypotheses.org 27
29. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Simulation Study
M = 1, 000 independent random samples {(Ui, Vi)}n
i=1 of sizes n = 200, n = 500
and n = 1000 were generated from each of the following copulas :
· the independence copula (i.e., Ui’s and Vi’s drawn independently) ;
· the Gaussian copula, with parameters ρ = 0.31, ρ = 0.59 and ρ = 0.81 ;
· the Student t-copula with 4 degrees of freedom, with parameters ρ = 0.31,
ρ = 0.59 and ρ = 0.81 ;
· the Frank copula, with parameter θ = 1.86, θ = 4.16 and θ = 7.93 ;
· the Gumbel copula, with parameter θ = 1.25, θ = 1.67 and θ = 2.5 ;
· the Clayton copula, with parameter θ = 0.5, θ = 1.67 and θ = 2.5.
(approximated) MISE relative to the MISE of the mirror-reflection estimator
(last column), n = 1000. Bold values show the minimum MISE for the
corresponding copula (non-significantly different values are highlighted as well).
@freakonometrics freakonometrics freakonometrics.hypotheses.org 29
31. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Probit Transform in the Univariate Case : the log transform
See Log-Transform Kernel Density Estimation of Income Distribution with E.
Flachaire
The Gaussian kernel estimator of a density is
fZ(z) =
1
n
n
i=1
ϕ(z; zi, h)
where ϕ(·; µ, σ) is the density of the normal distribution.
Use a Gaussian kernel estimation of the density using a
logarithmic transformation of data xi’s
fX(x) =
fZ(log(x))
x
=
1
n
n
i=1
h(x; log xi, h)
where h(·; µ, σ) is the density of the lognormal distribution.
@freakonometrics freakonometrics freakonometrics.hypotheses.org 31
32. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Probit Transform in the Univariate Case : the log transform
Recall that classically bias[fZ(z)] ∼
h2
2
fZ(z) and Var[fZ(z)] ∼
0.2821
nh
fZ(z)
Here, in the neighborhood of 0,
bias[fX(x)] ∼
h2
2
fX(x) + 3x · fX(x) + x2
· fX(x)
which is positive if fX(0) > 0, while
Var[fX(x)] ∼
0.2821
nhx
fX(x)
The log-transform kernel may perform
poorly when fX(0) > 0,
see Silverman (1986).
@freakonometrics freakonometrics freakonometrics.hypotheses.org 32
33. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Back on the Transformed Kernel
See Devroye & Györfi (1985), and Devroye & Lugosi (2001)
... use the transformed kernel the other way, R → [0, 1] → R
@freakonometrics freakonometrics freakonometrics.hypotheses.org 33
34. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Back on the Transformed Kernel
Interesting point, the optimal T should be F,
thus, T can be Fθ
@freakonometrics freakonometrics freakonometrics.hypotheses.org 34
35. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Heavy Tailed distribution
Let X denote a (heavy-tailed) random variable with tail index α ∈ (0, ∞), i.e.
P(X > x) = x−α
L1(x)
where L1 is some regularly varying function.
Let T denote a R → [0, 1] function, such that 1 − T is regularly varying at
infinity, with tail index β ∈ (0, ∞).
Define Q(x) = T−1
(1 − x−1
) the associated tail quantile function, then
Q(x) = x1/β
L2(1/x), where L2 is some regularly varying function (the de Bruyn
conjugate of the regular variation function associated with T). Assume here that
Q(x) = bx1/β
Let U = T(X). Then, as u → 1
P(U > u) ∼ (1 − u)α/β
.
@freakonometrics freakonometrics freakonometrics.hypotheses.org 35
36. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Heavy Tailed distribution
see C. & Oulidi (2010), α = 0.75−1
, T0.75−1 , T0.65−1
lighter
, T0.85−1
heavier
and Tˆα
Density
0.0 0.2 0.4 0.6 0.8 1.0
0.00.51.01.52.0
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
Density
0.0 0.2 0.4 0.6 0.8 1.0
0.00.51.01.52.0
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
Density
0.0 0.2 0.4 0.6 0.8 1.0
0.00.51.01.52.0
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
Density
0.0 0.2 0.4 0.6 0.8 1.0
0.00.51.01.52.0
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
@freakonometrics freakonometrics freakonometrics.hypotheses.org 36
37. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Heavy Tailed distribution
see C. & Oulidi (2007) Beta kernel quantile estimators of heavy-tailed loss
distributions, impact on quantile estimation ?
20 30 40 50 60 70 80
0.000.010.020.030.040.050.06
Density
@freakonometrics freakonometrics freakonometrics.hypotheses.org 37
38. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Heavy Tailed distribution
see C. & Oulidi (2007), impact on quantile estimation ? bias ? m.s.e. ?
@freakonometrics freakonometrics freakonometrics.hypotheses.org 38
39. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Bimodal distribution
Let X denote a bimodal distribution, obtained from a mixture
X ∼ FΘ
F0 if Θ = 0, (probability p0)
F1 if Θ = 1, (probability p1)
Idea : T(X) can be obtained as transformation of two distributions on [0, 1],
T(X) ∼ GΘ
G0 if Θ = 0, (probability p0)
G1 if Θ = 1, (probability p1)
→ standard for income observations...
@freakonometrics freakonometrics freakonometrics.hypotheses.org 39
40. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Which transformation ?
GB2 : t(y; a, b, p, q) =
|a|yap−1
bapB(p, q)[1 + (y/b)a]p+q
, for y > 0,
GB2
q→∞
~~
a=1
p=1
55
q=1
@@
GG
a→0
ÓÓ
a=1
p=1
%%
Beta2
q→∞
ww
SM
q→∞
xx
q=1
99
Dagum
p=1
Lognormal Gamma Weibull Champernowne
@freakonometrics freakonometrics freakonometrics.hypotheses.org 40
41. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Example, X ∼ Pareto
Pareto plot (log F(x) vs. log x), and histogram of {U1, · · · , Un}, Ui = Tˆθ(Xi)
log(x)
log(1−F(x))
1 5 10 50 100 500
5e−055e−045e−035e−025e−01
u=H(x)
densityg(u)
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.01.2
@freakonometrics freakonometrics freakonometrics.hypotheses.org 41
42. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Example, X ∼ Pareto
Estimation of the density g of U = Tθ
(X), and estimated c.d.f of X,
Fn(x) =
x
0
fn(y)dy where fn(y) = gn(Tˆθ(y)) · tˆθ(y)
u=H(x)
densityg(u)
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.01.2
Adaptative kernel
log(x)
log(1−F(x))
1 5 10 50 100 500
5e−055e−045e−035e−025e−01
@freakonometrics freakonometrics freakonometrics.hypotheses.org 42
43. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Beta kernel
g(u) =
n
i=1
1
n
· b u;
Ui
h
,
1 − Ui
h
u ∈ [0, 1].
with some possible boundary correction, as suggested in Chen (1999),
u
h
→ ρ(u, h) = 2h2
+ 2.5 − (4h4
+ 6h2
+ 2.25 − u2
− u/h)1/2
Problem : choice of the bandwidth h ? Standard loss function
L(h) = [gn(u) − g(u)]2
du = [gn(u)]2
du − 2 gn(u) · g(u)du
CV (h)
+ [g(u)]2
du
where
CV (h) = gn(u)du
2
−
2
n
n
i=1
g(−i)(Ui)
@freakonometrics freakonometrics freakonometrics.hypotheses.org 43
46. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Mixture of Beta distributions
g(u) =
k
j=1
πj · b u; αj, βj u ∈ [0, 1].
Problem : choice the number of components k (and estimation...). Use of
stochastic EM algorithm (or sort of) see Celeux Diebolt (1985).
@freakonometrics freakonometrics freakonometrics.hypotheses.org 46
48. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Mixture of Beta distributions
u=H(x)
densityg(u)
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.01.2
Beta mixtures
log(x)
log(1−F(x))
1 5 10 50 100 5001e−051e−041e−031e−021e−01
@freakonometrics freakonometrics freakonometrics.hypotheses.org 48
49. Arthur CHARPENTIER - Using Transformations of Variables to Improve Inference
Bernstein approximation
g(u) =
m
k=1
[mωk] · b (u; k, m − k) u ∈ [0, 1].
where ωk = G
k
m
− G
k − 1
m
.
@freakonometrics freakonometrics freakonometrics.hypotheses.org 49