Buch, Englisch, 233 Seiten, Format (B × H): 155 mm x 234 mm, Gewicht: 499 g
Buch, Englisch, 233 Seiten, Format (B × H): 155 mm x 234 mm, Gewicht: 499 g
ISBN: 978-1-84821-289-3
Verlag: Wiley
Statistical analysis of data sets usually involves construction of a statistical model of the distribution of data within the available sample – and by extension the distribution of all data of the same category in the world. Statistical models are either parametric or non-parametric – this distinction is based on whether or not the model can be described in terms of a finite-dimensional parameter – and the models must be tested to ascertain whether or not they conform to the data, or are accurate.
This book addresses the testing of hypotheses in non-parametric models in the specific case of censored or truncated data samples. In particular, the applicability of standard tests to incomplete data sets is considered – for example the use of the chi-squared test for parametric accelerated failure time regression models, which are widely used in reliability, accelerated life testing, and survival analysis, is detailed.
Classical non-parametric tests (goodness-of-fit, homogeneity, randomness, independence) of censored data are considered, and explained. Tests featured include the chi-squared and modified chi-squared tests, rank and homogeneity tests, and most of the test results are proved, with real applications illustrated using examples. The incorrect use of many tests, and their application using commonly deployed statistical software is highlighted and discussed.
Theories and exercises are provided, making this book suitable for use in a one semester course in non-parametric statistics and tests.
Autoren/Hrsg.
Weitere Infos & Material
Preface xi
Terms and Notation xv
Chapter 1. Censored and Truncated Data 1
1.1. Right-censored data 2
1.2. Left truncation 12
1.3. Left truncation and right censoring 14
1.4. Nelson–Aalen and Kaplan–Meier estimators 15
1.5 Bibliographic notes 17
Chapter 2. Chi-squared Tests 19
2.1. Chi-squared test for composite hypothesis 19
2.2. Chi-squared test for exponential distributions 31
2.3. Chi-squared tests for shape-scale distribution families 36
2.4. Chi-squared tests for other families 51
2.5. Exercises 59
2.6. Answers 60
Chapter 3. Homogeneity Tests for Independent Populations 63
3.1 Data 64
3.2 Weighted logrank statistics 64
3.3. Logrank test statistics as weighted sums of differences between observed and expected number of failures 66
3.4 Examples of weights 67
3.5. Weighted logrank statistics as modified score statistics 69
3.6. The first two moments of weighted logrank statistics 71
3.7. Asymptotic properties of weighted logrank statistics 73
3.8. Weighted logrank tests 80
3.9. Homogeneity testing when alternatives are crossings of survival functions 85
3.10. Exercises 98
3.11. Answers 102
Chapter 4. Homogeneity Tests for Related Populations 105
4.1. Paired samples 106
4.2. Logrank-type tests for homogeneity of related k > 2 samples 119
4.3. Homogeneity tests for related samples against crossing marginal survival functions alternatives 122
4.4. Exercises 125
4.5 Answers 126
Chapter 5. Goodness-of-fit for Regression Models 127
5.1. Goodness-of-fit for the semi-parametric Cox model 127
5.2. Chi-squared goodness-of-fit tests for parametric AFT models 142
5.3. Chi-squared test for the exponential AFT model 153
5.4. Chi-squared tests for scale-shape AFT models 159
Bibliographic notes 172
5.6. Exercises 173
Answers 174
APPENDICES 177
Appendix A. 179
Appendix B. 191
Appendix C. 211
Bibliography 225
Index 231
