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The Resource Analytical methods in statistics : AMISTAT, Prague, November 2015, Jaromír Antoch, Jana Jurečková, Matúš Maciak, Michal Pešta, editors

Analytical methods in statistics : AMISTAT, Prague, November 2015, Jaromír Antoch, Jana Jurečková, Matúš Maciak, Michal Pešta, editors

Label
Analytical methods in statistics : AMISTAT, Prague, November 2015
Title
Analytical methods in statistics
Title remainder
AMISTAT, Prague, November 2015
Statement of responsibility
Jaromír Antoch, Jana Jurečková, Matúš Maciak, Michal Pešta, editors
Creator
Contributor
Editor
Subject
Genre
Language
eng
Summary
This volume collects authoritative contributions on analytical methods and mathematical statistics. The methods presented include resampling techniques; the minimization of divergence; estimation theory and regression, eventually under shape or other constraints or long memory; and iterative approximations when the optimal solution is difficult to achieve. It also investigates probability distributions with respect to their stability, heavy-tailness, Fisher information and other aspects, both asymptotically and non-asymptotically. The book not only presents the latest mathematical and statistical methods and their extensions, but also offers solutions to real-world problems including option pricing. The selected, peer-reviewed contributions were originally presented at the workshop on Analytical Methods in Statistics, AMISTAT 2015, held in Prague, Czech Republic, November 10-13, 2015
Member of
Cataloging source
N$T
Dewey number
519.5
Index
no index present
LC call number
QA276.A1
Literary form
non fiction
http://bibfra.me/vocab/lite/meetingDate
2015
http://bibfra.me/vocab/lite/meetingName
Workshop Analytical Methods in Statistics
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorDate
1940-
http://library.link/vocab/relatedWorkOrContributorName
  • Antoch, Jaromír
  • Jurečková, Jana
  • Maciak, Matúš
  • Pešta, Michal
Series statement
Springer proceedings in mathematics & statistics,
Series volume
volume 193
http://library.link/vocab/subjectName
  • Mathematical statistics
  • Statistics
  • Statistical Theory and Methods
  • Probability Theory and Stochastic Processes
  • Statistics for Business/Economics/Mathematical Finance/Insurance
Label
Analytical methods in statistics : AMISTAT, Prague, November 2015, Jaromír Antoch, Jana Jurečková, Matúš Maciak, Michal Pešta, editors
Instantiates
Publication
Antecedent source
unknown
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 Families4.5 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 Results3.1 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
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9783319513133
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
10.1007/978-3-319-51313-3
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
ocn970693494
Label
Analytical methods in statistics : AMISTAT, Prague, November 2015, Jaromír Antoch, Jana Jurečková, Matúš Maciak, Michal Pešta, editors
Publication
Antecedent source
unknown
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 Families4.5 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 Results3.1 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
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9783319513133
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
10.1007/978-3-319-51313-3
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
ocn970693494

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