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M-statistics = optimal statistical inference for a small sample /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
M-statistics/ Eugene Demidenko.
其他題名:
optimal statistical inference for a small sample /
其他題名:
Mathematical statistics
作者:
Demidenko, Eugene,
出版者:
Hoboken, NJ :John Wiley & Sons, : c2023.,
面頁冊數:
1 online resource (xii, 222 p.) :ill. (some col.)
標題:
Mathematical statistics. -
電子資源:
https://onlinelibrary.wiley.com/doi/book/10.1002/9781119891826
ISBN:
9781119891826
M-statistics = optimal statistical inference for a small sample /
Demidenko, Eugene,1948-
M-statistics
optimal statistical inference for a small sample /[electronic resource] :Mathematical statisticsEugene Demidenko. - 1st ed. - Hoboken, NJ :John Wiley & Sons,c2023. - 1 online resource (xii, 222 p.) :ill. (some col.)
Includes bibliographical references and index.
"M-statistics: A New Statistical Perspective introduces a new approach for statistical interference, redesigning the fundamentals of statistics and improving on the classical methods we already use. The author discusses the development of new criteria for efficient estimation and delves into how two methods for statistical intereference are combined under one umbrella to create 'M statistics.' This book develops novel confidence intervals and statistical tests for statistical parameters including effect size, binomial probability, and Poisson rate, ensuring unbiased tests are developed alongside this. Suitable for professionals and students alike, this theoretical book explains how new approaches work for statistical applications and is accompanied with a GitHub repository hosting the R code for every new methodology presented."--
ISBN: 9781119891826Subjects--Topical Terms:
516858
Mathematical statistics.
LC Class. No.: QA276.A2 / D46 2023
Dewey Class. No.: 519.5/4
M-statistics = optimal statistical inference for a small sample /
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"M-statistics: A New Statistical Perspective introduces a new approach for statistical interference, redesigning the fundamentals of statistics and improving on the classical methods we already use. The author discusses the development of new criteria for efficient estimation and delves into how two methods for statistical intereference are combined under one umbrella to create 'M statistics.' This book develops novel confidence intervals and statistical tests for statistical parameters including effect size, binomial probability, and Poisson rate, ensuring unbiased tests are developed alongside this. Suitable for professionals and students alike, this theoretical book explains how new approaches work for statistical applications and is accompanied with a GitHub repository hosting the R code for every new methodology presented."--
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https://onlinelibrary.wiley.com/doi/book/10.1002/9781119891826
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