Książka Theoretical Statistics Robert W. Keener

Theoretical Statistics

Topics for a Core Course

Język: Angielski
Oprawa: Miękka
Wydawca: Springer, Berlin
Dostępność: Dostępna u dostawcy
Wysyłamy za 10-18 dni
727.20
Intended as the text for a sequence of advanced courses, this book covers major topics in theoretica...

Informacje o książce

Język
Angielski
Oprawa
Książka - Miękka
Data wydania
2012
strony
538
EAN
9781461426707
ISBN
1461426707
Enbook ID
01427434
Waga
818
Wymiary
156 x 31 x 234

Pełny opis

Intended as the text for a sequence of advanced courses, this book covers major topics in theoretical statistics in a concise and rigorous fashion. The discussion assumes a background in advanced calculus, linear algebra, probability, and some analysis and topology. Measure theory is used, but the notation and basic results needed are presented in an initial chapter on probability, so prior knowledge of these topics is not essential.§The presentation is designed to expose students to as many of the central ideas and topics in the discipline as possible, balancing various approaches to inference as well as exact, numerical, and large sample methods. Moving beyond more standard material, the book includes chapters introducing bootstrap methods, nonparametric regression, equivariant estimation, empirical Bayes, and sequential design and analysis.§The book has a rich collection of exercises. Several of them illustrate how the theory developed in the book may be used in various applications. Solutions to many of the exercises are included in an appendix.§Robert Keener is Professor of Statistics at the University of Michigan and a fellow of the Institute of Mathematical Statistics.Intended as the text for a sequence of advanced courses, this book covers major topics in theoretical statistics in a concise and rigorous fashion. The discussion assumes a background in advanced calculus, linear algebra, probability, and some analysis and topology. Measure theory is used, but the notation and basic results needed are presented in an initial chapter on probability, so prior knowledge of these topics is not essential.§The presentation is designed to expose students to as many of the central ideas and topics in the discipline as possible, balancing various approaches to inference as well as exact, numerical, and large sample methods. Moving beyond more standard material, the book includes chapters introducing bootstrap methods, nonparametric regression, equivariant estimation, empirical Bayes, and sequential design and analysis.§The book has a rich collection of exercises. Several of them illustrate how the theory developed in the book may be used in various applications. Solutions to many of the exercises are included in an appendix.

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