Elements Of Computational Statistics

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Elements of Computational Statistics

Author	: James E. Gentle
Publisher	: Springer Science & Business Media
Total Pages	: 427
Release	: 2006-04-18
ISBN-10	: 9780387216119
ISBN-13	: 0387216111
Rating	: 4/5 (111 Downloads)

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Book Synopsis Elements of Computational Statistics by : James E. Gentle

Download or read book Elements of Computational Statistics written by James E. Gentle and published by Springer Science & Business Media. This book was released on 2006-04-18 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: Will provide a more elementary introduction to these topics than other books available; Gentle is the author of two other Springer books

Elements of Computational Statistics Related Books

Language: en
Pages: 427

Elements of Computational Statistics

Authors: James E. Gentle

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Type: BOOK - Published: 2006-04-18 - Publisher: Springer Science & Business Media

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Will provide a more elementary introduction to these topics than other books available; Gentle is the author of two other Springer books

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Basic Elements of Computational Statistics

Authors: Wolfgang Karl Härdle

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This textbook on computational statistics presents tools and concepts of univariate and multivariate statistical data analysis with a strong focus on applicatio

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Type: BOOK - Published: 2017-10-19 - Publisher: Routledge

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Statistics and computing share many close relationships. Computing now permeates every aspect of statistics, from pure description to the development of statist

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Computational Statistics

Authors: Geof H. Givens

Categories: Mathematics

Type: BOOK - Published: 2012-10-09 - Publisher: John Wiley & Sons

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This new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing. The book is comprised of four main parts

Language: en
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Computational Statistics

Authors: James E. Gentle

Categories: Mathematics

Type: BOOK - Published: 2009-07-28 - Publisher: Springer Science & Business Media

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Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators