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Mathematical Statistics: Asymptotic Minimax Theory

โœ Scribed by Alexander Korostelev, Olga Korosteleva


Publisher
American Mathematical Society
Year
2011
Tongue
English
Leaves
258
Series
Graduate Studies in Mathematics 119
Category
Library

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โœฆ Synopsis


This book is designed to bridge the gap between traditional textbooks in statistics and more advanced books that include the sophisticated nonparametric techniques. It covers topics in parametric and nonparametric large-sample estimation theory. The exposition is based on a collection of relatively simple statistical models. It gives a thorough mathematical analysis for each of them with all the rigorous proofs and explanations. The book also includes a number of helpful exercises. Prerequisites for the book include senior undergraduate/beginning graduate-level courses in probability and statistics


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Mathematical statistics. Asymptotic mini
โœ Korostelev A.P., Korosteleva O. ๐Ÿ“‚ Library ๐Ÿ“… 2011 ๐Ÿ› American Mathematical Society ๐ŸŒ English

This book is designed to bridge the gap between traditional textbooks in statistics and more advanced books that include the sophisticated nonparametric techniques. It covers topics in parametric and nonparametric large-sample estimation theory. The exposition is based on a collection of relatively

Mathematical Statistics: Asymptotic Mini
โœ Alexander Korostelev, Olga Korosteleva ๐Ÿ“‚ Library ๐Ÿ“… 2011 ๐Ÿ› American Mathematical Society ๐ŸŒ English

This book is designed to bridge the gap between traditional textbooks in statistics and more advanced books that include the sophisticated nonparametric techniques. It covers topics in parametric and nonparametric large-sample estimation theory. The exposition is based on a collection of relatively

Mathematical Statistics: Asymptotic Mini
โœ Alexander Korostelev, Olga Korosteleva ๐Ÿ“‚ Library ๐Ÿ“… 2011 ๐Ÿ› American Mathematical Society ๐ŸŒ English

<span>This book is designed to bridge the gap between traditional textbooks in statistics and more advanced books that include the sophisticated nonparametric techniques. It covers topics in parametric and nonparametric large-sample estimation theory. The exposition is based on a collection of relat