Nonparametric Tests for Complete Data
1st Edition
Published on 7. December 2010
Book
Hardback
326 pages
978-1-84821-269-5 (ISBN)
Description
This book concerns testing hypotheses in non-parametric models. Classical non-parametric tests (goodness-of-fit, homogeneity, randomness, independence) of complete data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided. The incorrect use of many tests applying most statistical software is highlighted and discussed.
More details
Product info
GB
Edition
1., Auflage
Language
English
Place of publication
London
United Kingdom
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 234 mm
Width: 157 mm
Thickness: 23 mm
Weight
617 gr
ISBN-13
978-1-84821-269-5 (9781848212695)
Schweitzer Classification
Other editions
Additional editions
Vilijandas Bagdonavièus | Julius Kruopis | Mikhail Nikulin
Nonparametric Tests for Complete Data
E-Book
02/2013
Wiley-ISTE
€149.99
Available for download
Vilijandas Bagdonavièus | Julius Kruopis | Mikhail Nikulin
Nonparametric Tests for Complete Data
E-Book
02/2013
Wiley-ISTE
€149.99
Available for download
Persons
Vilijandas Bagdonavicius is Professor of Mathematics at the University of Vilnius in Lithuania. His main research areas are statistics, reliability and survival analysis.
Julius Kruopis is Associate Professor of Mathematics at the University of Vilnius in Lithuania. His main research areas are statistics and quality control.
Mikhail S. Nikulin is a member of the Institute of Mathematics in Bordeaux, France.
Julius Kruopis is Associate Professor of Mathematics at the University of Vilnius in Lithuania. His main research areas are statistics and quality control.
Mikhail S. Nikulin is a member of the Institute of Mathematics in Bordeaux, France.
Content
Preface xi
Terms and Notation xv
Chapter 1. Introduction 1
1.1. Statistical hypotheses 1
1.2. Examples of hypotheses in non-parametric models 2
1.3. Statistical tests 5
1.4. P-value 7
1.5. Continuity correction 10
1.6. Asymptotic relative efficiency 13
Chapter 2. Chi-squared Tests 17
2.1. Introduction 17
2.2. Pearson's goodness-of-fit test: simple hypothesis 17
2.3. Pearson's goodness-of-fit test: composite hypothesis 26
2.4. Modified chi-squared test for composite hypotheses 34
2.5. Chi-squared test for independence 52
2.6. Chi-squared test for homogeneity 57
2.7. Bibliographic notes 64
2.8. Exercises 64
2.9. Answers 72
Chapter 3. Goodness-of-fit Tests Based on Empirical Processes 77
3.1. Test statistics based on the empirical process 77
3.2. Kolmogorov-Smirnov test 82
3.3. ?2, Cramer-von-Mises and Andersen-Darling tests 86
3.4. Modifications of Kolmogorov-Smirnov, Cramer-von-Mises and Andersen-Darling tests: composite
hypotheses 91
3.5. Two-sample tests 98
3.6. Bibliographic notes 104
3.7. Exercises106
3.8. Answers 109
Chapter 4. Rank Tests 111
4.1. Introduction 111
4.2. Ranks and their properties 112
4.3. Rank tests for independence 117
4.4. Randomness tests 139
4.5. Rank homogeneity tests for two independent samples 146
4.6. Hypothesis on median value: the Wilcoxon signed ranks test 168
4.7. Wilcoxon's signed ranks test for homogeneity of two related samples 180
4.8. Test for homogeneity of several independent samples: Kruskal-Wallis test 181
4.9. Homogeneity hypotheses for k related samples: Friedman test 191
4.10. Independence test based on Kendall's concordance coefficient 204
4.11. Bibliographic notes 208
4.12. Exercises 209
4.13. Answers 212
Chapter 5. Other Non-parametric Tests 215
5.1. Sign test 215
5.2. Runs test 221
5.3. McNemar's test 231
5.4. Cochran test 238
5.5. Special goodness-of-fit tests 245
5.6. Bibliographic notes 268
5.7. Exercises 269
5.8. Answers 271
APPENDICES 275
Appendix A. Parametric Maximum Likelihood 277
Appendix B. Notions from the Theory of 281
BBibliography 293
Index 305
Terms and Notation xv
Chapter 1. Introduction 1
1.1. Statistical hypotheses 1
1.2. Examples of hypotheses in non-parametric models 2
1.3. Statistical tests 5
1.4. P-value 7
1.5. Continuity correction 10
1.6. Asymptotic relative efficiency 13
Chapter 2. Chi-squared Tests 17
2.1. Introduction 17
2.2. Pearson's goodness-of-fit test: simple hypothesis 17
2.3. Pearson's goodness-of-fit test: composite hypothesis 26
2.4. Modified chi-squared test for composite hypotheses 34
2.5. Chi-squared test for independence 52
2.6. Chi-squared test for homogeneity 57
2.7. Bibliographic notes 64
2.8. Exercises 64
2.9. Answers 72
Chapter 3. Goodness-of-fit Tests Based on Empirical Processes 77
3.1. Test statistics based on the empirical process 77
3.2. Kolmogorov-Smirnov test 82
3.3. ?2, Cramer-von-Mises and Andersen-Darling tests 86
3.4. Modifications of Kolmogorov-Smirnov, Cramer-von-Mises and Andersen-Darling tests: composite
hypotheses 91
3.5. Two-sample tests 98
3.6. Bibliographic notes 104
3.7. Exercises106
3.8. Answers 109
Chapter 4. Rank Tests 111
4.1. Introduction 111
4.2. Ranks and their properties 112
4.3. Rank tests for independence 117
4.4. Randomness tests 139
4.5. Rank homogeneity tests for two independent samples 146
4.6. Hypothesis on median value: the Wilcoxon signed ranks test 168
4.7. Wilcoxon's signed ranks test for homogeneity of two related samples 180
4.8. Test for homogeneity of several independent samples: Kruskal-Wallis test 181
4.9. Homogeneity hypotheses for k related samples: Friedman test 191
4.10. Independence test based on Kendall's concordance coefficient 204
4.11. Bibliographic notes 208
4.12. Exercises 209
4.13. Answers 212
Chapter 5. Other Non-parametric Tests 215
5.1. Sign test 215
5.2. Runs test 221
5.3. McNemar's test 231
5.4. Cochran test 238
5.5. Special goodness-of-fit tests 245
5.6. Bibliographic notes 268
5.7. Exercises 269
5.8. Answers 271
APPENDICES 275
Appendix A. Parametric Maximum Likelihood 277
Appendix B. Notions from the Theory of 281
BBibliography 293
Index 305