| Author: | Zbynek Sidak, Pranab K. Sen, Jaroslav Hajek | ISBN: | 9780080519104 |
| Publisher: | Elsevier Science | Publication: | April 6, 1999 |
| Imprint: | Academic Press | Language: | English |
| Author: | Zbynek Sidak, Pranab K. Sen, Jaroslav Hajek |
| ISBN: | 9780080519104 |
| Publisher: | Elsevier Science |
| Publication: | April 6, 1999 |
| Imprint: | Academic Press |
| Language: | English |
The first edition of Theory of Rank Tests (1967) has been the precursor to a unified and theoretically motivated treatise of the basic theory of tests based on ranks of the sample observations. For more than 25 years, it helped raise a generation of statisticians in cultivating their theoretical research in this fertile area, as well as in using these tools in their application oriented research. The present edition not only aims to revive this classical text by updating the findings but also by incorporating several other important areas which were either not properly developed before 1965 or have gone through an evolutionary development during the past 30 years. This edition therefore aims to fulfill the needs of academic as well as professional statisticians who want to pursue nonparametrics in their academic projects, consultation, and applied research works.
- Asymptotic Methods
- Nonparametrics
- Convergence of Probability Measures
- Statistical Inference
The first edition of Theory of Rank Tests (1967) has been the precursor to a unified and theoretically motivated treatise of the basic theory of tests based on ranks of the sample observations. For more than 25 years, it helped raise a generation of statisticians in cultivating their theoretical research in this fertile area, as well as in using these tools in their application oriented research. The present edition not only aims to revive this classical text by updating the findings but also by incorporating several other important areas which were either not properly developed before 1965 or have gone through an evolutionary development during the past 30 years. This edition therefore aims to fulfill the needs of academic as well as professional statisticians who want to pursue nonparametrics in their academic projects, consultation, and applied research works.
- Asymptotic Methods
- Nonparametrics
- Convergence of Probability Measures
- Statistical Inference
