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.
General
| Imprint: |
Springer Nature Switzerland AG
|
| Country of origin: |
Switzerland |
| Series: |
Springer Series in the Data Sciences |
| Release date: |
December 2018 |
| First published: |
2018 |
| Authors: |
Mayer Alvo
• Philip L.H. Yu
|
| Dimensions: |
279 x 210 x 16mm (L x W x T) |
| Format: |
Paperback
|
| Pages: |
279 |
| Edition: |
Softcover reprint of the original 1st ed. 2018 |
| ISBN-13: |
978-3-03-006804-2 |
| Categories: |
Books >
Science & Mathematics >
Mathematics >
Probability & statistics
Promotions
|
| LSN: |
3-03-006804-8 |
| Barcode: |
9783030068042 |
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