Nonparametric statistical methods. Myles Hollander, Douglas A. Wolfe and Eric Chicken.
Material type:
TextSeries: Wiley series in probability and statisticsPublication details: New Jersey : John Wiley, c2014.Edition: 3rd edDescription: xiii, 819 p. : illustrations ; 27 cmISBN: - 9780470387375 (hardback)
- 23 H737 000SA.12
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 000SA.12 H737 (Browse shelf(Opens below)) | Available | 135812 | |||
| Books | ISI Library, Kolkata | 000SA.12 H737 (Browse shelf(Opens below)) | Available | C26288 |
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| 000SA.12 C435 Nonparametric methods for data in infinite dimensional space / | 000SA.12 D456 Nonparametric statistics : | 000SA.12 G874 Nonparametric estimation under shape constraints : estimators, algorithms, and asymptotics / | 000SA.12 H737 Nonparametric statistical methods. | 000SA.12 H737 Nonparametric statistical methods. | 000SA.12 J61 Nonparametric statistical methods and related topics : | 000SA.12 K66 Nonparametric statistical methods using R / |
Includes bibliographical references (pages 763-790) and indexes.
1. Introduction--
2. The dichotomous data problem--
3. The one-sample location problem--
4. The two-sample location problem--
5. The two-sample dispersion problem and other two-sample problems--
6. The one-way layout--
7. The two-way layout--
8. The independence problem--
9. Regression problems--
10. Comparing two success probabilities--
11. Life distributions and survival analysis--
12. Density estimation--
13. Wavelets--
14. Smoothing--
15. Ranked set sampling--
16. An introduction to Bayesian nonparametric statistics via the Dirichlet process--
Bibliography--
R Program Index--
Author Index--
Subject Index.
"Written by leading statisticians, this new edition has been completely updated to include additional modern topics and procedures, more real-world data sets, and more problems from real-life situations. Incorporating the R software program, this user-friendly book provides readers with an arsenal of nonparametric techniques, helping them develop the insight needed to choose appropriate procedures for various situations. It features five new chapters with added-on topics including Density Estimation, Kernel Regression, Nonparametric Regression, Ranked-Set Sampling, and Bayesian Nonparametrics"--
"In this third edition we have improved the eleven chapters of the second edition and added five new chapters. Also organized all of the R programs used in this third edition into a documented collection that is formally registered as an R package specifically linked to this third edition. "--
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