Cover -- Title page -- Table of Contents -- Preface -- Biography of Mikhail Stepanovitch Nikouline -- PART 1. STATISTICAL MODELS AND METHODS -- Chapter 1. Unidimensionality, Agreement and Concordance Probability -- 1.1. Introduction -- 1.2. From reliability to unidimensionality: CAC and curve -- 1.2.1. Classical unidimensional models for measurement -- 1.2.2. Reliability of an instrument: CAC -- 1.2.3. Unidimensionality of an instrument: BRC -- 1.3. Agreement between binary outcomes: the kappa coefficient -- 1.3.1. The kappa model -- 1.3.2. The kappa coefficient -- 1.3.3. Estimation of the kappa coefficient -- 1.4. Concordance probability -- 1.4.1. Relationship with Kendall's τ measure -- 1.4.2. Relationship with Somer's D measure -- 1.4.3. Relationship with ROC curve -- 1.5. Estimation and inference -- 1.6. Measure of agreement -- 1.7. Extension to survival data -- 1.7.1. Harrell's c-index -- 1.7.2. Measure of discriminatory power -- 1.8. Discussion -- 1.9. Bibliography -- Chapter 2. A Universal Goodness-of-Fit Test Based on Regression Techniques -- 2.1. Introduction -- 2.2. The Brain and Shapiro procedure for the exponential distribution -- 2.3. Applications of the Brain and Shapiro test -- 2.4. Small sample null distribution of the test statistic for specific distributions -- 2.5. Power studies -- 2.6. Some real examples -- 2.7. Conclusions -- 2.8. Acknowledgment -- 2.9. Bibliography -- Chapter 3. Entropy-type Goodness-of-Fit Tests for Heavy-Tailed Distributions -- 3.1. Introduction -- 3.2. The entropy test for heavy-tailed distributions -- 3.2.1. Development and asymptotic theory -- 3.2.2. Discussion -- 3.3. Simulation study -- 3.4. Conclusions -- 3.5. Bibliography -- Chapter 4. Penalized Likelihood Methodology and Frailty Models -- 4.1. Introduction -- 4.2. Penalized likelihood in frailty models for clustered data.

4.2.1. Gamma distributed frailty -- 4.2.2. Inverse Gaussian distributed frailty -- 4.2.3. Uniform distributed frailty -- 4.3. Simulation results -- 4.4. Concluding remarks -- 4.5. Bibliography -- Chapter 5. Interactive Investigation of Statistical Regularities in Testing Composite Hypotheses of Goodness of Fit -- 5.1. Introduction -- 5.2. Distributions of the test statistics in the case of testing composite hypotheses -- 5.3. Testing composite hypotheses in "real-time" -- 5.4. Conclusions -- 5.5. Acknowledgment -- 5.6. Bibliography -- Chapter 6. Modeling of Categorical Data -- 6.1. Introduction -- 6.2. Continuous conditional distributions -- 6.2.1. Conditional normal distribution -- 6.2.1.1. Estimation of parameters -- 6.2.2. More general continuous conditional distributions -- 6.2.2.1. Conditional distribution -- 6.2.2.2. Normal copula -- 6.3. Discrete conditional distributions -- 6.3.1. Parametric conditional distributions -- 6.3.2. Estimation of parameters -- 6.4. Goodness of fit -- 6.4.1. Distribution of ˆX2 -- 6.5. Modeling of categorical data -- 6.5.1. Contingency tables -- 6.5.1.1. General tables -- 6.5.1.2. Further examples -- 6.6. Bibliography -- Chapter 7. Within the Sample Comparison of Prediction Performance of Models and Submodels: Application to Alzheimer's Disease -- 7.1. Introduction -- 7.2. Framework -- 7.2.1. General description of the data set and the models to be compared -- 7.2.2. Definition of the performance prediction criteria: IDI and BRI -- 7.3. Estimation of IDI and BRI -- 7.3.1. General estimating equations for IDI and BRI -- 7.3.2. Estimation of IDI and BRI in the logistic case -- 7.3.2.1. Asymptotics of IDI2/1 for logistic predictors -- 7.3.2.2. Asymptotics of BRI2/1 for logistic predictors -- 7.4. Simulation studies -- 7.4.1. First simulation -- 7.4.2. Second simulation: Gu and Pepe's example.

7.5. The three city study of Alzheimer's disease -- 7.6. Conclusion -- 7.7. Bibliography -- Chapter 8. Durbin-Knott Components and Transformations of the Cramér-von Mises Test -- 8.1. Introduction -- 8.2. Weighted Cramér-von Mises statistic -- 8.3. Examples of the Cramér-von Mises statistics -- 8.3.1. Classical Cramér-von Mises statistic -- 8.3.2. Anderson-Darling statistic -- 8.3.3. Cramér-von Mises statistic with the power weight function -- 8.4. Weighted parametric Cramér-von Mises statistic -- 8.4.1. Covariance functions of weighted parametric empirical process -- 8.4.2. Eigenvalues and eigenfunctions for weighted parametric Cramérvon Mises statistic -- 8.5. Transformations of the Cramér-von Mises statistic -- 8.5.1. Preliminary notes -- 8.5.2. Replacement of eigenvalues -- 8.5.3. Transformed statistics -- 8.6. Bibliography -- Chapter 9. Conditional Inference in Parametric Models -- 9.1. Introduction and context -- 9.2. The approximate conditional density of the sample -- 9.2.1. Approximation of conditional densities -- 9.2.2. The proxy of the conditional density of the sample -- 9.2.3. Comments on implementation -- 9.3. Sufficient statistics and approximated conditional density -- 9.3.1. Keeping sufficiency under the proxy density -- 9.3.2. Rao-Blackwellization -- 9.4. Exponential models with nuisance parameters -- 9.4.1. Conditional inference in exponential families -- 9.4.2. Application of conditional sampling to MC tests -- 9.4.2.1. Context -- 9.4.2.2. Bimodal likelihood: testing the mean of a normal distribution in dimension -- 9.4.3. Estimation through conditional likelihood -- 9.5. Bibliography -- Chapter 10. On Testing Stochastic Dominance by Exceedance, Precedence and Other Distribution-Free Tests, with Applications -- 10.1. Introduction -- 10.2. Results -- 10.2.1. The experimental data set.

10.2.2. An application of the Wilcoxon-Mann-Whitney statistics -- 10.2.3. One-sided Kolmogorov-Smirnov tests -- 10.2.4. Precedence and Exceedance Tests -- 10.3. Negative binomial limit laws -- 10.4. Conclusion -- 10.5. Bibliography -- Chapter 11. Asymptotically Parameter-Free Tests for Ergodic Diffusion Processes -- 11.1. Introduction -- 11.2. Ergodic diffusion process and some limits -- 11.3. Shift parameter -- 11.4. Shift and scale parameters -- 11.5. Bibliography -- Chapter 12. A Comparison of Homogeneity Tests for Different Alternative Hypotheses -- 12.1. Homogeneity tests -- 12.1.1. Tests for data without censoring -- 12.1.2. Tests for data with censoring -- 12.2. Alternative hypotheses -- 12.3. Power simulation -- 12.3.1. Power of tests without censoring -- 12.3.2. Power of tests with censoring -- 12.3.2.1. How does the distribution of censoring time affect the power of the test? -- 12.3.2.2. How does the censoring rate affect the power of the test? -- 12.4. Statistical inference -- 12.5. Acknowledgment -- 12.6. Bibliography -- Chapter 13. Some Asymptotic Results for Exchangeably Weighted Bootstraps of the Empirical Estimator of a Semi-Markov Kernel with Applications -- 13.1. Introduction -- 13.2. Semi-Markov setting -- 13.3. Main results -- 13.4. Bootstrap for a multidimensional empirical estimator of a continuoustime semi-Markov kernel -- 13.5. Confidence intervals -- 13.6. Bibliography -- Chapter 14. On Chi-Squared Goodness-of-Fit Test for Normality -- 14.1. Chi-squared test for normality -- 14.2. Simulation study -- 14.3. Bibliography -- PART 2. STATISTICAL MODELS AND METHODS IN SURVIVAL ANALYSIS -- Chapter 15. Estimation/Imputation Strategies for Missing Data in Survival Analysis -- 15.1. Introduction -- 15.2. Model and strategies -- 15.2.1. Model assumptions -- 15.2.2. Strategy involving knowledge of ζ.

15.2.3. Strategy involving knowledge of π -- 15.2.4. Estimation of ζ or π : logit or non-parametric regression -- 15.2.5. Computing the hazard estimators -- 15.2.6. Theoretical results -- 15.3. Imputation-based strategy -- 15.4. Numerical comparison -- 15.5. Proofs -- 15.6. Bibliography -- Chapter 16. Non-Parametric Estimation of Linear Functionals of a Multivariate Distribution Under Multivariate Censoring with Applications -- 16.1. Introduction -- 16.2. Non-parametric estimation of the distribution -- 16.3. Asymptotic properties -- 16.4. Statistical applications of functionals -- 16.4.1. Dependence measures -- 16.4.2. Bootstrap -- 16.4.3. Linear regression -- 16.5. Illustration -- 16.6. Conclusion -- 16.7. Acknowledgment -- 16.8. Bibliography -- Chapter 17. Kernel Estimation of Density from Indirect Observation -- 17.1. Introduction -- 17.1.1. Random partition -- 17.1.2. Indirect observation -- 17.1.3. Kernel density estimator -- 17.2. Density of random vector Λ(X) -- 17.3. Pseudo-kernel density estimator -- 17.3.1. Pointwise density estimation based on indirect data -- 17.3.2. Bias of the kernel estimator -- 17.3.3. Estimate of variance -- 17.4. Bibliography -- Chapter 18. A Comparative Analysis of Some Chi-Square Goodness-of-Fit Tests for Censored Data -- 18.1. Introduction -- 18.2. Chi-square goodness-of-fit tests for censored data -- 18.2.1. NRR χ2 test -- 18.2.2. GPF χ2 test -- 18.3. The choice of grouping intervals -- 18.3.1. Equifrequent grouping (EFG) -- 18.3.2. Intervals with equal expected numbers of failures (EENFG) -- 18.3.3. Optimal grouping (OptG) -- 18.4. Empirical power study -- 18.5. Conclusions -- 18.6. Acknowledgment -- 18.7. Bibliography -- Chapter 19. A Non-parametric Test for Comparing Treatments with Missing Data and Dependent Censoring -- 19.1. Introduction -- 19.2. The proposed test statistic.

19.3. Asymptotic distribution of the proposed test statistic.