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A Parametric Approach to Nonparametric Statistics

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This book demonstrates that nonparametric statistics can be taught from a parametric point of view. As a result, one can exploit various parametric tools such as the use of the likelihood function, penalized likelihood and score functions to not only derive well-known tests but to also go beyond and make use of Bayesian methods to analyze ranking data.

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This book demonstrates that nonparametric statistics can be taught from a parametric point of view. As a result, one can exploit various parametric tools such as the use of the likelihood function, penalized likelihood and score functions to not only derive well-known tests but to also go beyond and make use of Bayesian methods to analyze ranking data.

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Book Format

Hardcover, Softcover

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Book information

Edition
1st Edition
ISBN [Hardcover]
9783319941523
ISBN [Softcover]
9783030068042
Publisher
Springer
Year
2018
Pages
XIV, 279 p.
Series Title
Springer Series in the Data Sciences
Language
English
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