A parametric approach to nonparametric statistics / Mayer Alvo.
Material type:
TextPublisher: New York, NY : Springer Science+Business Media, 2018Description: pages cmContent type: - text
- computer
- online resource
- 9783319941523
| Item type | Current library | Call number | Status | Date due | Barcode | |
|---|---|---|---|---|---|---|
| E-Resources | Main Library E-Resources | 519.54 AL476 (Browse shelf(Opens below)) | Available | E002835 |
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| 519.536 W147 Bayesian and frequentist regression methods | 519.53622 B512 Statistical learning from a regression perspective / | 519.538 V299 Variant construction from theoretical foundation to applications | 519.54 AL476 A parametric approach to nonparametric statistics / | 519.542 M337 Bayesian essentials with R / | 519.55 B864 Introduction to time series and forecasting / | 519.55 L973 New introduction to multiple time series analysis |
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.
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