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Topics in Nonparametric Estimation
Edited by: R. Z. Khasminskiĭ
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Advances in Soviet Mathematics
1992; 150 pp; hardcover
Volume: 12
ISBN-10: 0-8218-4111-4
ISBN-13: 978-0-8218-4111-2
List Price: US$120
Member Price: US$96
Order Code: ADVSOV/12
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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 mid-1960s, 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.

Readership

Scientists and mathematicians in probability, statistics, and approximation theory. Computer scientists interested in image analysis.

Table of Contents

  • A. Samarov -- Lower bound for the integral risk of density function estimates
  • A. S. Nemirovskiĭ -- On nonparametric estimation of functions satisfying differential inequalities
  • A. P. Korostelev and A. B. Tsybakov -- Asymptotically minimax image reconstruction problems
  • O. V. Lepskiĭ -- On problems of adaptive estimation in white Gaussian noise
  • B. T. Polyak and A. B. Tsybakov -- On stochastic approximation with arbitrary noise (the KW-case)
  • E. N. Belitser and A. P. Korostelev -- Pseudovalues and minimax filtering algorithms for the nonparametric median
  • A. Yu. Veretennikov -- On large deviations for ergodic process empirical measures
  • V. G. Spokoĭnyĭ -- On asymptotically optimal sequential experimental design
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