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The Resource Analytical Methods in Statistics : AMISTAT, Prague, November 2015

Analytical Methods in Statistics : AMISTAT, Prague, November 2015

Label
Analytical Methods in Statistics : AMISTAT, Prague, November 2015
Title
Analytical Methods in Statistics
Title remainder
AMISTAT, Prague, November 2015
Creator
Contributor
Subject
Language
eng
Member of
Cataloging source
MiAaPQ
Literary form
non fiction
Nature of contents
dictionaries
Series statement
Springer Proceedings in Mathematics and Statistics Ser.
Series volume
v.193
Analytical Methods in Statistics : AMISTAT, Prague, November 2015
Label
Analytical Methods in Statistics : AMISTAT, Prague, November 2015
Link
http://libproxy.rpi.edu/login?url=https://ebookcentral.proquest.com/lib/rpi/detail.action?docID=4793350
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Copyright
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Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Preface -- Contents -- Contributors -- A Weighted Bootstrap Procedure for Divergence Minimization Problems -- 1 The Scope of This Paper -- 1.1 Existing Solutions for Similar Problems -- 2 Divergences -- 3 Large Deviations for the Bootstrapped Empirical Measure -- 3.1 Minimizing the Kullback--Leibler Divergence -- 3.2 Minimizing the Likelihood Divergence -- 4 Wild Bootstrap -- 4.1 A Conditional LDP for the Wild Bootstrapped Empirical Measure -- 4.2 Cressie--Read Divergences and Exponential Families -- 4.3 Natural Exponential Families and Their Variance Functions -- 4.4 Power Variance Functions and the Corresponding Natural Exponential Families -- 4.5 Cressie--Read Divergences, Weights and Variance Functions -- 4.6 Examples -- 5 Monte Carlo Minimization of a Cressie--Read Divergence Through Wild Bootstrap -- 6 Sets of Measures for Which the Monte Carlo Minimization Technique Applies -- 7 A Simple Convergence Result and Some Perspectives -- References -- Asymptotic Analysis of Iterated 1-Step Huber-Skip M-Estimators with Varying Cut-Offs -- 1 Introduction -- 2 Model and Outlier Detection Algorithms -- 2.1 Model -- 2.2 The Iterated 1-Step Huber-Skip M-Estimator Algorithm -- 3 The Main Results -- 3.1 Assumptions -- 3.2 Properties of the Iterated Estimators -- 3.3 Properties of the Gauge -- 4 Weighted and Marked Empirical Process -- 4.1 The Case of Estimated Scale and Known Regression Parameter -- 4.2 The Case of Estimated Scale and Regression Parameter -- 4.3 A Result for the Two-Sided Empirical Process -- 5 Discussion -- Appendix 1A metric on R and some inequalities -- Appendix 2 Proofs of empirical process results concerning scale -- Appendix 3 Proofs of empirical process results -- Appendix 4 Proofs of the main results -- References -- Regression Quantile and Averaged Regression Quantile Processes -- 1 Introduction
  • 2 Averaged Regression Quantile Process -- 3 Averaged Two-Step Regression Quantile Process -- References -- Stability and Heavy-Tailness -- 1 Introduction: A Little Bit of Naive Philosophy -- 2 Polya Theorem -- 3 p-normality and p-stability -- 4 Further Examples of p-Gaussian Distributions -- 5 Toy-Model of Capital Distribution -- 6 Few Words on the Distribution of Asset Returns -- References -- Smooth Estimation of Error Distribution in Nonparametric Regression Under Long Memory -- 1 Introduction -- 2 Main Results -- 3 Estimation of d and c(z) -- References -- Testing Shape Constraints in Lasso Regularized Joinpoint Regression -- 1 Introduction -- 2 Sobolev Spaces' Framework -- 2.1 Construction of Representors in Sobolev Space -- 3 Penalized Least Squares -- 3.1 Choice of the Smoothing Parameter -- 4 Application to Option Prices -- 4.1 State Price Density -- 4.2 Call and Put Options -- 5 Covariance Structure -- 5.1 Constant SPD -- 5.2 Dependencies Due to the Time of the Trade -- 6 DAX Option Prices -- 7 Conclusions -- References -- 1 Introduction -- 2 Joinpoint Regression Model with Shape Constrains -- 3 Statistical Test for Testing Shape Constraints Validity -- 3.1 Significancy Test for LASSO Regularized Estimates -- 3.2 Shape Constraints Inference for LASSO Joinpoint Models -- 4 Finite Sample Results -- 5 Conclusion -- References -- On Existence of Explicit Asymptotically Normal Estimators in Nonlinear Regression Problems -- 1 Introduction -- 2 Fractional-Linear Regression -- 3 Partially Linear Regression -- 4 Power Regression of Order 1/2 -- 5 Logarithmic Regression -- 6 General Power Regression -- 7 Exponential Regression -- 8 General Remarks About One-Dimensional Estimators -- 9 Multidimensional Case -- 10 Michaelis--Menten Equation -- 11 Proofs -- References -- On the Behavior of the Risk of a LASSO-Type Estimator -- 1 Introduction
  • 2 Model, Assumptions and Some Basic Results -- 3 The SVD-LASSO -- 4 Computation of the Risk -- 4.1 Two Basic Results -- References
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{'f': 'http://opac.lib.rpi.edu/record=b4393403'}
Extent
1 online resource (214 pages)
Form of item
online
Isbn
9783319513133
Media category
computer
Media MARC source
rdamedia
Media type code
c
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unknown sound
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remote

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