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Titlebook: Expository Moments for Pseudo Distributions; Haruhiko Ogasawara Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive

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發(fā)表于 2025-3-21 20:05:01 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Expository Moments for Pseudo Distributions
編輯Haruhiko Ogasawara
視頻videohttp://file.papertrans.cn/320/319758/319758.mp4
概述Shows explications and extensions of the pseudo normal (PN) family of distributions recently‘proposed by the author.Gives the moments of the PN without omitting proofs with didactic explanations using
叢書(shū)名稱Behaviormetrics: Quantitative Approaches to Human Behavior
圖書(shū)封面Titlebook: Expository Moments for Pseudo Distributions; Haruhiko Ogasawara Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive
描述.This book provides expository derivations for moments of a family of pseudo distributions, which is an extended family of distributions including the pseudo normal (PN) distributions recently proposed by the author. The PN includes the skew normal (SN) derived by A. Azzalini and the closed skew normal (CSN) obtained by A. Domínguez-Molina, G. González-Farías, and A. K. Gupta as special cases. It is known that the CSN includes the SN and other various distributions as special cases, which shows that the PN has a wider variety of distributions. The SN and CSN have symmetric and skewed asymmetric distributions. However, symmetric distributions are restricted to normal ones. On the other hand, symmetric distributions in the PN can be non-normal as well as normal. In this book, for the non-normal symmetric distributions, the term “kurtic normal (KN)” is used, where the coined word “kurtic” indicates “mesokurtic, leptokurtic, or platykurtic” used in statistics. The variety of the PN was made possible using stripe (tigerish) and sectional truncation in univariate and multivariate distributions, respectively. The proofs of the moments and associated results are not omitted and are often g
出版日期Book 2022
關(guān)鍵詞Stripe Truncation; Sectional Truncation; Pseudo Normal; Pseudo Distributions; Skew Normal; Closed Skew No
版次1
doihttps://doi.org/10.1007/978-981-19-3525-1
isbn_softcover978-981-19-3527-5
isbn_ebook978-981-19-3525-1Series ISSN 2524-4027 Series E-ISSN 2524-4035
issn_series 2524-4027
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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The Pseudo-Normal (PN) Distribution,rmal (CSN), where the CSN is an extension of the SN and includes various important distributions. Note that the SN and CSN use single truncation in typically hidden truncation models while the PN uses sectional truncation introduced by Ogasawara (Ogasawara in Journal of Multivariate Analysis (online
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發(fā)表于 2025-3-22 12:26:28 | 只看該作者
The Kurtic-Normal (KN) Distribution, platykurtic” used in statistics. Note that in the skew-normal (SN) and closed skew-normal (CSN), when a distribution is symmetric, it is always normal. On the other hand, though the distribution of KN is symmetric, it is not necessarily normal. Moments and cumulants for some simple KN distributions
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發(fā)表于 2025-3-22 13:31:46 | 只看該作者
The Normal-Normal (NN) Distribution,normal distribution in place of the usual normal under sectional truncation. Though the discrete normal distribution takes normal densities at finite/infinite points approximating the truncated normal, the discrete distribution is not necessarily a truncated one. Their moment generating function and
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The Truncated Pseudo-Normal (TPN) and Truncated Normal-Normal (TNN) Distributions, typically hidden variable(s) in the PN are subject to truncation except the untruncated cases giving observable normal distributions of less interest. When the observable variables are sectionally truncated, we have the truncated pseudo normal (TPN) and normal-normal (TNN). The properties of the TP
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Multivariate Measures of Skewness and Kurtosis,trix (commutator) . with several explicit expressions of . are presented, where the methods of proofs using the elements in matrix equations are emphasized with the by-products of the explicit expressions of the elements. The vectors of the multivariate cumulants up to the fourth order are shown by
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