Contents -- Foreword -- Preface -- Overview -- Part I False Discovery Rates -- 1. A New Adaptive Method to Control the False Discovery Rate Fang Liu and Sanat K. Sarkar -- 1.1. Introduction -- 1.2. Notation, Definition and Formulas -- 1.3. A Review of FDR Controlling Methods -- 1.3.1. The BH method -- 1.3.2. The adaptive BH method of Benjamini & Hochberg -- 1.3.3. The adaptive BH method of Storey, Taylor and Siegmund -- 1.3.4. The adaptive BH method of Benjamini, Krieger and Yekutieli -- 1.3.5. The adaptive method of Gavrilov, Benjamini and Sarkar -- 1.4. A New Estimate of n0 -- 1.4.1. The estimate -- 1.4.2. Simulation study -- 1.5. New Adaptive Method to Control the FDR -- 1.5.1. The new adaptive BH method -- 1.5.2. Simulation study -- 1.6. An Application to Breast Cancer Data -- 1.7. Concluding Remarks -- Acknowledgment -- References -- 2. Adaptive Multiple Testing Procedures Under Positive Dependence Wenge Guo, Sanat K. Sarkar and Shyamal D. Peddada -- 2.1. Introduction -- 2.2. Adaptive Global Tests -- 2.3. Adaptive Multiple Testing Procedures -- 2.3.1. The procedures -- 2.3.2. An application -- 2.3.3. A simulation study -- 2.4. Concluding Remarks -- References -- 3. A False Discovery Rate Procedure for Categorical Data Joseph F. Heyse -- 3.1. Introduction -- 3.2. False Discovery Rate -- 3.3. Modified FDR Procedure for Categorical Data -- 3.4. Tarone and Gilbert Modified Procedures -- 3.4.1. Discrete modification to Bonferroni adjustment -- 3.5. Illustration -- 3.6. Application: Genetic Variants of HIV -- 3.7. Simulation Study -- 3.8. Concluding Remarks -- References -- Part II Survival Analysis -- 4. Conditional Nelson-Aalen and Kaplan-Meier Estimators with the M uller-Wang Boundary Kernel Xiaodong Luo and Wei-Yann Tsai -- 4.1. Introduction -- 4.2. The Boundary Kernel -- 4.3. The Estimators -- 4.4. Proofs -- 4.4.1. Preliminaries and notation.

4.4.2. Proof of Theorem 4.1 -- 4.4.3. Proof of Theorem 4.3 -- 4.4.4. Proof of Theorem 4.3 -- 4.5. Discussions -- Acknowledgments -- References -- 5. Regression Analysis in Failure Time Mixture Models with Change Points According to Thresholds of a Covariate Jimin Lee, Thomas H. Scheike and Yanqing Sun -- 5.1. Introduction -- 5.2. Model Descriptions -- 5.3. The EM Algorithm -- 5.4. Hypothesis Tests of Change-points -- 5.5. Simulation Studies -- 5.6. Application to the Melanoma Survival Study -- 5.7. Discussion -- 5.8. Acknowledgments -- References -- 6. Modeling Survival Data Using the Piecewise Exponential Model with Random Time Grid Fabio N. Demarqui, Dipak K. Dey, Rosangela H. Loschi, and Enrico A. Colosimo -- 6.1. Introduction -- 6.2. Model Construction -- 6.2.1. Piecewise exponential distribution and the likelihood -- 6.2.2. Priors and the clustering structure -- 6.2.3. Posterior distributions and related inference -- 6.3. Numerical Illustration -- 6.4. Conclusions -- Acknowledgments -- References -- 7. Proportional Rate Models for Recurrent Time Event Data Under Dependent Censoring: A Comparative Study Leila D. A. F Amorim, Jianwen Cai and Donglin Zeng -- 7.1. Introduction -- 7.2. Modeling Recurrent Event Data -- 7.2.1. Notation and proportional rate model -- 7.2.2. WQC method -- 7.2.3. MKLB method -- 7.2.4. WQC vs MKLB method -- 7.3. Simulation Framework -- 7.4. Simulation Results -- 7.5. An Example: Modeling Times to Recurrent Diarrhea in Children -- 7.6. Concluding Remarks -- Acknowledgments -- References -- 8. Efficient Algorithms for Bayesian Binary Regression Model with Skew-Probit Link Rafael B. A. Farias and Marcia D. Branco -- 8.1. Introduction -- 8.2. Symmetric Models and the Use of Latent Variables -- 8.2.1. Probit regression -- 8.3. Skew-Probit Regression -- 8.3.1. A general class of skewed links.

8.3.2. Bayesian regression with skew-probit link -- 8.4. New Simulation Algorithms -- 8.4.1. Analytical results -- 8.4.2. Joint updating of {z, } -- 8.4.3. Joint updating of {z,w} -- 8.4.4. Joint updating of {z, } -- 8.4.5. Joint updating of {z, , } -- 8.5. Comparison between Algorithms -- 8.5.1. Efficiency analysis with known skewness -- 8.5.2. Efficiency analysis with unknown skewness parameter -- 8.6. Application -- 8.7. Conclusion -- Acknowledgment -- Appendix. -- A.1. Proofs of Propositions -- A.2. Pseudo-codes -- References -- 9. M-Estimation Methods in Heteroscedastic Nonlinear Regression Models Changwon Lim, Pranab K. Sen and Shyamal D. Peddada -- 9.1. Introduction -- 9.2. Definitions and Regularity Conditions -- 9.3. Asymptotic Results -- 9.4. Illustration -- 9.5. Concluding Remarks and Ongoing Research -- Acknowledgments -- Appendix. Proof of Lemmas -- A.1. Proof of Lemma 9.1 -- A.2. Proof of Lemma 9.2 -- References -- 10. The Inverse Censoring Weighted Approach for Estimation of Survival Functions from Left and Right Censored Data Sundarraman Subramanian and Peixin Zhang -- 10.1. Introduction -- 10.2. Survival Function Estimators -- 10.2.1. The KM type estimator -- 10.2.2. The ICW estimator -- 10.3. An Illustration -- 10.4. Numerical Results -- 10.5. Concluding Discussion -- Acknowledgments -- Appendix. -- A.1. The ICW Type I Estimator -- References -- 11. Analysis and Design of Competing Risks Data in Clinical Research Haesook T. Kim -- 11.1. Introduction -- 11.2. Estimation and Comparison of Cumulative Incidence Curves -- 11.3. Competing Risks Regression Analysis -- 11.3.1. Fine and Gray model -- 11.4. Power Calculation -- 11.5. Computing Tools -- 11.6. Conclusion -- Acknowledgments -- References -- Part III Related Topics: Genomics/Bioinformatics, Medical Imaging and Diagnosis, Clinical Trials.

12. Comparative Genomic Analysis Using Information Theory Sarosh N. Fatakia, Stefano Costanzi and Carson C. Chow -- 12.1. Introduction -- 12.2. Materials and Methods -- 12.2.1. Multi-sequence alignments -- 12.2.2. Shannon entropy and mutual information -- 12.2.3. MI graphs -- 12.2.4. Key position identification -- 12.3. Results -- 12.4. Discussion -- 12.5. Acknowledgments -- References -- 13. Statistical Modeling for Data of Positron Emission Tomography in Depression Chung Chang and R. Todd Ogden -- 13.1. Introduction to PET Imaging and Its Application to Depression -- 13.2. PET Data and Acquisition of PET Imaging -- 13.2.1. PET data -- 13.2.2. Acquisition of PET imaging -- 13.3. Modeling of Voxel-specific Time Series -- 13.3.1. Compartment models -- 13.3.2. Modeling plasma model and correcting the plasma data for metabolites -- 13.3.3. Basis pursuit -- 13.3.4. Graphical model -- Acknowledgments -- References -- 14. The Use of Latent Class Analysis in Medical Diagnosis David Rindskopf -- 14.1. Latent Class Analysis: A Brief Overview -- 14.2. A Simple Example: Myocardial Infarction -- 14.3. Conditional Dependence Models -- 14.4. Latent Class Analysis with a Categorical Predictor of Class: Wheeze as an Indicator of Asthma in Children -- 14.5. Logistic Regression with Floor and Ceiling Effects -- 14.6. Discussion -- Acknowledgments -- References -- 15. Subset Selection in Comparative Selection Trials Cheng-Shiun Leu, Ying Kuen Cheung and Bruce Levin -- 15.1. Introduction -- 15.2. Terminology and Procedures -- 15.2.1. Notation -- 15.2.2. Procedures and properties -- 15.2.3. Example -- 15.3. Simulation Studies -- 15.4. Discussion -- Acknowledgment -- References -- Index.