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Stochastic comparisons with applicationsin order statistics and spacings /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Stochastic comparisons with applicationsby Subhash C. Kochar.
其他題名:	in order statistics and spacings /
作者:	Kochar, Subhash C.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xvi, 272 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Stochastic analysis.
電子資源:	https://doi.org/10.1007/978-3-031-12104-3
ISBN:	9783031121043$q(electronic bk.)

Stochastic comparisons with applicationsin order statistics and spacings /
Kochar, Subhash C.

Stochastic comparisons with applicationsin order statistics and spacings /[electronic resource] :by Subhash C. Kochar. - Cham :Springer International Publishing :2022. - xvi, 272 p. :ill. (some col.), digital ;24 cm.

Introduction and Preliminaries -- Magnitude Orders -- Variability Orders -- Skewness and Relative Aging Orders -- Dependence Orders -- Stochastic comparisons of sample spacings -- Dependence among order statistics and spacings -- Stochastic Comparisons of Weighted Sums of Random Variables -- Stochastic comparisons of mixtures of distributions.

This book emphasizes the use of stochastic orders as motivational tools for developing new statistical procedures. Stochastic orders have found useful applications in many disciplines, including reliability theory, survival analysis, risk theory, finance, nonparametric methods, economics and actuarial science. Written by a statistician, this volume clarifies the connection between stochastic orders and nonparametric methods. The importance of order statistics and spacings is well recognized. Classically, they mainly focus on the case when the observations are independent and identically distributed, however, several new developments have extended the comparison of order statistics to the case of non-identically distributed or non-independent observations. In addition to giving a detailed discussion of various topics in the general area of stochastic orders, a substantial part of the book is devoted to recent research on stochastic comparisons of order statistics and spacings, including a long chapter on dependence among them. The book will be useful for graduate students and researchers in statistics, economics, actuarial science and other related disciplines. In particular, with close to 300 references, it will be a valuable resource for reliability theorists, applied probabilists and statisticians. Readers are expected to have taken a first-year graduate level course in mathematical statistics or in applied probability.

ISBN: 9783031121043$q(electronic bk.)

Standard No.: 10.1007/978-3-031-12104-3doiSubjects--Topical Terms:

183332
Stochastic analysis.

LC Class. No.: QA274.2 / .K63 2022

Dewey Class. No.: 519.22

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505 0 $a Introduction and Preliminaries -- Magnitude Orders -- Variability Orders -- Skewness and Relative Aging Orders -- Dependence Orders -- Stochastic comparisons of sample spacings -- Dependence among order statistics and spacings -- Stochastic Comparisons of Weighted Sums of Random Variables -- Stochastic comparisons of mixtures of distributions.
520 $a This book emphasizes the use of stochastic orders as motivational tools for developing new statistical procedures. Stochastic orders have found useful applications in many disciplines, including reliability theory, survival analysis, risk theory, finance, nonparametric methods, economics and actuarial science. Written by a statistician, this volume clarifies the connection between stochastic orders and nonparametric methods. The importance of order statistics and spacings is well recognized. Classically, they mainly focus on the case when the observations are independent and identically distributed, however, several new developments have extended the comparison of order statistics to the case of non-identically distributed or non-independent observations. In addition to giving a detailed discussion of various topics in the general area of stochastic orders, a substantial part of the book is devoted to recent research on stochastic comparisons of order statistics and spacings, including a long chapter on dependence among them. The book will be useful for graduate students and researchers in statistics, economics, actuarial science and other related disciplines. In particular, with close to 300 references, it will be a valuable resource for reliability theorists, applied probabilists and statisticians. Readers are expected to have taken a first-year graduate level course in mathematical statistics or in applied probability.
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