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Fundamental statistical inference = a computational approach /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Fundamental statistical inference/ Marc S. Paolella.
其他題名:	a computational approach /
作者:	Paolella, Marc S.
出版者:	Hoboken, NJ :Wiley, : 2018.,
面頁冊數:	1 online resource.
標題:	Mathematical statistics. -
電子資源:	https://onlinelibrary.wiley.com/doi/book/10.1002/9781119417897
ISBN:	9781119417897

Fundamental statistical inference = a computational approach /
Paolella, Marc S.

Fundamental statistical inferencea computational approach /[electronic resource] :Marc S. Paolella. - Hoboken, NJ :Wiley,2018. - 1 online resource. - Wiley series in probability and statistics. - Wiley series in probability and statistics..

Includes bibliographical references and index.

A hands-on approach to statistical inference that addresses the latest developments in this ever-growing field This clear and accessible book for beginning graduate students offers a practical and detailed approach to the field of statistical inference, providing complete derivations of results, discussions, and MATLAB programs for computation. It emphasizes details of the relevance of the material, intuition, and discussions with a view towards very modern statistical inference. In addition to classic subjects associated with mathematical statistics, topics include an intuitive presentation of the (single and double) bootstrap for confidence interval calculations, shrinkage estimation, tail (maximal moment) estimation, and a variety of methods of point estimation besides maximum likelihood, including use of characteristic functions, and indirect inference. Practical examples of all methods are given. Estimation issues associated with the discrete mixtures of normal distribution, and their solutions, are developed in detail. Much emphasis throughout is on non-Gaussian distributions, including details on working with the stable Paretian distribution and fast calculation of the noncentral Student's t. An entire chapter is dedicated to optimization, including development of Hessian-based methods, as well as heuristic/genetic algorithms that do not require continuity, with MATLAB codes provided. The book includes both theory and nontechnical discussions, along with a substantial reference to the literature, with an emphasis on alternative, more modern approaches. The recent literature on the misuse of hypothesis testing and p-values for model selection is discussed, and emphasis is given to alternative model selection methods, though hypothesis testing of distributional assumptions is covered in detail, notably for the normal distribution.

ISBN: 9781119417897Subjects--Topical Terms:

516858
Mathematical statistics.

LC Class. No.: QA276 / .P365 2018

Dewey Class. No.: 519.54

Fundamental statistical inference = a computational approach /
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245 1 0 $a Fundamental statistical inference $h [electronic resource] : $b a computational approach / $c Marc S. Paolella.
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300 $a 1 online resource.
490 1 $a Wiley series in probability and statistics
504 $a Includes bibliographical references and index.
520 $a A hands-on approach to statistical inference that addresses the latest developments in this ever-growing field This clear and accessible book for beginning graduate students offers a practical and detailed approach to the field of statistical inference, providing complete derivations of results, discussions, and MATLAB programs for computation. It emphasizes details of the relevance of the material, intuition, and discussions with a view towards very modern statistical inference. In addition to classic subjects associated with mathematical statistics, topics include an intuitive presentation of the (single and double) bootstrap for confidence interval calculations, shrinkage estimation, tail (maximal moment) estimation, and a variety of methods of point estimation besides maximum likelihood, including use of characteristic functions, and indirect inference. Practical examples of all methods are given. Estimation issues associated with the discrete mixtures of normal distribution, and their solutions, are developed in detail. Much emphasis throughout is on non-Gaussian distributions, including details on working with the stable Paretian distribution and fast calculation of the noncentral Student's t. An entire chapter is dedicated to optimization, including development of Hessian-based methods, as well as heuristic/genetic algorithms that do not require continuity, with MATLAB codes provided. The book includes both theory and nontechnical discussions, along with a substantial reference to the literature, with an emphasis on alternative, more modern approaches. The recent literature on the misuse of hypothesis testing and p-values for model selection is discussed, and emphasis is given to alternative model selection methods, though hypothesis testing of distributional assumptions is covered in detail, notably for the normal distribution.
588 $a Description based on print version record.
650 0 $a Mathematical statistics. $3 516858
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