Characterizations Bivariate Multivariate Matrix by Ghosh Indranil (6 results)
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Taschenbuch. Condition: Neu. Characterizations of bivariate, multivariate and matrix variate | normal distributions | Indranil Ghosh (u. a.) | Taschenbuch | 68 S. | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786200537133 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt,… info[at]bod[dot]de | Anbieter: preigu.
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Condition: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | This monograph is a collective effort by Hamedani and Ghosh which focuses on a topic which has appeared in many forms: journal articles, short reviews, books; i.e., characteri- zations of bivariate, multivariate and matrix variate normal distributi…ons. The first author is thankful to the second author for his continuous encouragement of invaluable suggestions in preparing this short monograph. This monograph attempts to collect all the characteriza- tions results related to a Gaussian distribution in bivariate and in higher domains that exist in the literature consequently claims its uniqueness. It also tries to paint some sketches of the proofs to some of the characterization results that are not available in the literature as of yet to the best of the knowledge of the authors. However, the authors do believe that contributors to this subject matter might have these ideas in their mind but did not feel nec- essary to pen it down. While this monograph heavily draws on articles by the second author, Hamedani and his co-authors in a series of papers, we sincerely hope that the readers and practitioners in need of an appropriate Gaussian distribution will find it very useful.
Language: English
Published by LAP LAMBERT Academic Publishing Jan 2020, 2020
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This monograph is a collective effort by Hamedani and Ghosh which focuses on a topic which has appeared in many forms: journal articles, short reviews, books; i.e., characteri- zations of bivariate, multivariate and matrix variate n…ormal distributions. The first author is thankful to the second author for his continuous encouragement of invaluable suggestions in preparing this short monograph. This monograph attempts to collect all the characteriza- tions results related to a Gaussian distribution in bivariate and in higher domains that exist in the literature consequently claims its uniqueness. It also tries to paint some sketches of the proofs to some of the characterization results that are not available in the literature as of yet to the best of the knowledge of the authors. However, the authors do believe that contributors to this subject matter might have these ideas in their mind but did not feel nec- essary to pen it down. While this monograph heavily draws on articles by the second author, Hamedani and his co-authors in a series of papers, we sincerely hope that the readers and practitioners in need of an appropriate Gaussian distribution will find it very useful. 68 pp. Englisch.
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ghosh IndranilDr. Hamedani is a Professor at Marquette Univ., Milwaukee, Wisconsin, USA. He received his PhD from Michigan State University in 1971. He is Editor of the Journal of Statistical Theory and… Applications since 2004 and me.
Language: English
Published by LAP LAMBERT Academic Publishing Jan 2020, 2020
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This monograph is a collective effort by Hamedani and Ghosh which focuses on a topic which has appeared in many forms: journal articles, short reviews, books; i.e., characteri- zations of bivariate, multivariate and matrix variate norma…l distributions. The first author is thankful to the second author for his continuous encouragement of invaluable suggestions in preparing this short monograph. This monograph attempts to collect all the characteriza- tions results related to a Gaussian distribution in bivariate and in higher domains that exist in the literature consequently claims its uniqueness. It also tries to paint some sketches of the proofs to some of the characterization results that are not available in the literature as of yet to the best of the knowledge of the authors. However, the authors do believe that contributors to this subject matter might have these ideas in their mind but did not feel nec- essary to pen it down. While this monograph heavily draws on articles by the second author, Hamedani and his co-authors in a series of papers, we sincerely hope that the readers and practitioners in need of an appropriate Gaussian distribution will find it very useful.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 68 pp. Englisch.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This monograph is a collective effort by Hamedani and Ghosh which focuses on a topic which has appeared in many forms: journal articles, short reviews, books; i.e., characteri- zations of bivariate, multivariate and matrix variate normal… distributions. The first author is thankful to the second author for his continuous encouragement of invaluable suggestions in preparing this short monograph. This monograph attempts to collect all the characteriza- tions results related to a Gaussian distribution in bivariate and in higher domains that exist in the literature consequently claims its uniqueness. It also tries to paint some sketches of the proofs to some of the characterization results that are not available in the literature as of yet to the best of the knowledge of the authors. However, the authors do believe that contributors to this subject matter might have these ideas in their mind but did not feel nec- essary to pen it down. While this monograph heavily draws on articles by the second author, Hamedani and his co-authors in a series of papers, we sincerely hope that the readers and practitioners in need of an appropriate Gaussian distribution will find it very useful.
