Hardcover ISBN:  9780821841112 
Product Code:  ADVSOV/12 
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AMS Member Price:  $106.40 
eBook ISBN:  9781470446093 
Product Code:  ADVSOV/12.E 
List Price:  $133.00 
MAA Member Price:  $119.70 
AMS Member Price:  $106.40 
Hardcover ISBN:  9780821841112 
eBook: ISBN:  9781470446093 
Product Code:  ADVSOV/12.B 
List Price:  $266.00 $199.50 
MAA Member Price:  $239.40 $179.55 
AMS Member Price:  $212.80 $159.60 
Hardcover ISBN:  9780821841112 
Product Code:  ADVSOV/12 
List Price:  $133.00 
MAA Member Price:  $119.70 
AMS Member Price:  $106.40 
eBook ISBN:  9781470446093 
Product Code:  ADVSOV/12.E 
List Price:  $133.00 
MAA Member Price:  $119.70 
AMS Member Price:  $106.40 
Hardcover ISBN:  9780821841112 
eBook ISBN:  9781470446093 
Product Code:  ADVSOV/12.B 
List Price:  $266.00 $199.50 
MAA Member Price:  $239.40 $179.55 
AMS Member Price:  $212.80 $159.60 

Book DetailsAdvances in Soviet MathematicsVolume: 12; 1992; 150 ppMSC: Primary 60; 62
This book contains papers presented at the Seminar on Mathematical Statistics held at the Institute for Problems of Information Transmission of the Academy of Sciences in the former Soviet Union. Founded in the mid1960s, this seminar is still active today and attracts most of the researchers in Moscow who are interested in mathematical statistics. The topics covered include density, regression, and image estimation, adaptive estimation, stochastic approximation, median estimation, sequential experimental design, and large deviations for empirical measures. This collection is distinguished by the high scientific level of the papers and their modern approach. This book will be of interest to scientists and engineers who use probability and statistics, to mathematicians and applied statisticians who work in approximation theory, and to computer scientists who work in image analysis.
ReadershipScientists and mathematicians in probability, statistics, and approximation theory. Computer scientists interested in image analysis.

Table of Contents

Articles

A. Samarov — Lower bound for the integral risk of density function estimates

A. Nemirovskii — On nonparametric estimation of functions satisfying differential inequalities

A. Korostelev and A. Tsybakov — Asymptotically minimax image reconstruction problems

O. Lepskii — On problems of adaptive estimation in white Gaussian noise

B. Polyak and A. Tsybakov — On stochastic approximation with arbitrary noise (the KWcase)

E. Belitser and A. Korostelev — Pseudovalues and minimax filtering algorithms for the nonparametric median

A. Veretennikov — On large deviations for ergodic process empirical measures

V. Spokoinyi — On asymptotically optimal sequential experimental design


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This book contains papers presented at the Seminar on Mathematical Statistics held at the Institute for Problems of Information Transmission of the Academy of Sciences in the former Soviet Union. Founded in the mid1960s, this seminar is still active today and attracts most of the researchers in Moscow who are interested in mathematical statistics. The topics covered include density, regression, and image estimation, adaptive estimation, stochastic approximation, median estimation, sequential experimental design, and large deviations for empirical measures. This collection is distinguished by the high scientific level of the papers and their modern approach. This book will be of interest to scientists and engineers who use probability and statistics, to mathematicians and applied statisticians who work in approximation theory, and to computer scientists who work in image analysis.
Scientists and mathematicians in probability, statistics, and approximation theory. Computer scientists interested in image analysis.

Articles

A. Samarov — Lower bound for the integral risk of density function estimates

A. Nemirovskii — On nonparametric estimation of functions satisfying differential inequalities

A. Korostelev and A. Tsybakov — Asymptotically minimax image reconstruction problems

O. Lepskii — On problems of adaptive estimation in white Gaussian noise

B. Polyak and A. Tsybakov — On stochastic approximation with arbitrary noise (the KWcase)

E. Belitser and A. Korostelev — Pseudovalues and minimax filtering algorithms for the nonparametric median

A. Veretennikov — On large deviations for ergodic process empirical measures

V. Spokoinyi — On asymptotically optimal sequential experimental design