Table of contents
About this book
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. The book bridges the gap between parametric and nonparametric statistics and presents the best practices of the former while enjoying the robustness properties of the latter.
This book can be used in a graduate course in nonparametrics, with parts being accessible to senior undergraduates. In addition, the book will be of wide interest to statisticians and researchers in applied fields.
- DOI https://doi.org/10.1007/978-3-319-94153-0
- Copyright Information Springer Nature Switzerland AG 2018
- Publisher Name Springer, Cham
- eBook Packages Mathematics and Statistics
- Print ISBN 978-3-319-94152-3
- Online ISBN 978-3-319-94153-0
- Series Print ISSN 2365-5674
- Series Online ISSN 2365-5682
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