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Table of Contents
Intro; Preface; Part I: Review and Theoretical Framework in Biostatistics (Chaps. 1 -4); Part II: Wavelet-Based Approach for Complex Data (Chaps. 5 -8); Part III: Clinical Trials and Statistical Modeling (Chaps. 9 -14); Part IV: High-Dimensional Gene Expression Data Analysis (Chaps. 15 -18); Part V: Survival Analysis (Chaps. 19 -22); Contents; List of Contributors; List of Chapter Reviewers; About the Editors; Part I Review of Theoretical Framework in Biostatistics; 1 Optimal Weighted Wilcoxon-Mann-Whitney Testfor Prioritized Outcomes; 1.1 Introduction
1.2 Wilcoxon-Mann-Whitney Test for Prioritized Endpoints1.2.1 Notation; 1.2.2 Wilcoxon-Mann-Whitney Test; 1.2.3 Weighted Wilcoxon-Mann-Whitney Test; 1.2.3.1 Prespecified Weights; 1.2.3.2 Optimal Weights; 1.3 Simulation Studies; 1.4 Application to a Stroke Clinical Trial; 1.5 Discussion; Appendix; Appendix 1: Proof of Theorem 1.1; Appendix 2: Mean and Variance of the Weighted U-Statistic; Appendix 3: Optimal Weights; Appendix 4: Conditional Probabilities; Exponential Distribution; Normal Distribution; References; 2 A Selective Overview of Semiparametric Mixtureof Regression Models
2.1 Introduction2.2 Mixture of Regression Models with Varying Proportions; 2.2.1 Continuous Response, p=1; 2.2.2 Continuous Response, p>1; 2.2.3 Discrete Response; 2.3 Nonparametric Errors; 2.3.1 Semiparametric EM Algorithm with Kernel Density Error; 2.3.2 Log-Concave Density Error; 2.3.3 Mixtures of Quantile Regressions; 2.4 Semiparametric Mixture of Nonparametric Regressions; 2.4.1 Nonparametric Mixture of Regressions; 2.4.2 Nonparametric Component Regression Functions; 2.4.3 Mixture of Regressions with Single-Index; 2.5 Semiparametric Regression Models for Longitudinal/Functional Data
2.5.1 Mixture of Time-Varying Effects for Intensive Longitudinal Data2.5.2 Mixtures of Gaussian Processes; 2.5.3 Mixture of Functional Linear Models; 2.6 Some Additional Topics; 2.7 Discussion; References; 3 Rank-Based Empirical Likelihood for Regression Modelswith Responses Missing at Random; 3.1 Introduction; 3.2 Imputation; 3.2.1 Imputation Under MAR; 3.2.2 Empirical Likelihood Method; 3.3 Simulation Study; 3.3.1 Simulation Settings; 3.3.2 Real Data; 3.4 Conclusion; Appendix; Assumptions; References; 4 Bayesian Nonparametric Spatially Smoothed Density Estimation; 4.1 Introduction
4.2 The Predictive Model4.2.1 Markov Chain Monte Carlo; 4.2.2 Censored Data; 4.2.3 Direct Estimation and a Permutation Test p-Value; 4.3 Examples; 4.3.1 IgG Distribution Evolving with Age; 4.3.2 Time to Infection in Amphibian Populations; 4.3.3 Simulated Data; 4.4 Conclusion; References; Part II Wavelet-Based Approach for Complex Data; 5 Mammogram Diagnostics Using Robust Wavelet-Based Estimator of Hurst Exponent; 5.1 Introduction; 5.2 Background; 5.2.1 Non-decimated Wavelet Transforms; 5.2.2 The fBm: Wavelet Coefficients and Spectra; 5.3 General Trimean Estimators
1.2 Wilcoxon-Mann-Whitney Test for Prioritized Endpoints1.2.1 Notation; 1.2.2 Wilcoxon-Mann-Whitney Test; 1.2.3 Weighted Wilcoxon-Mann-Whitney Test; 1.2.3.1 Prespecified Weights; 1.2.3.2 Optimal Weights; 1.3 Simulation Studies; 1.4 Application to a Stroke Clinical Trial; 1.5 Discussion; Appendix; Appendix 1: Proof of Theorem 1.1; Appendix 2: Mean and Variance of the Weighted U-Statistic; Appendix 3: Optimal Weights; Appendix 4: Conditional Probabilities; Exponential Distribution; Normal Distribution; References; 2 A Selective Overview of Semiparametric Mixtureof Regression Models
2.1 Introduction2.2 Mixture of Regression Models with Varying Proportions; 2.2.1 Continuous Response, p=1; 2.2.2 Continuous Response, p>1; 2.2.3 Discrete Response; 2.3 Nonparametric Errors; 2.3.1 Semiparametric EM Algorithm with Kernel Density Error; 2.3.2 Log-Concave Density Error; 2.3.3 Mixtures of Quantile Regressions; 2.4 Semiparametric Mixture of Nonparametric Regressions; 2.4.1 Nonparametric Mixture of Regressions; 2.4.2 Nonparametric Component Regression Functions; 2.4.3 Mixture of Regressions with Single-Index; 2.5 Semiparametric Regression Models for Longitudinal/Functional Data
2.5.1 Mixture of Time-Varying Effects for Intensive Longitudinal Data2.5.2 Mixtures of Gaussian Processes; 2.5.3 Mixture of Functional Linear Models; 2.6 Some Additional Topics; 2.7 Discussion; References; 3 Rank-Based Empirical Likelihood for Regression Modelswith Responses Missing at Random; 3.1 Introduction; 3.2 Imputation; 3.2.1 Imputation Under MAR; 3.2.2 Empirical Likelihood Method; 3.3 Simulation Study; 3.3.1 Simulation Settings; 3.3.2 Real Data; 3.4 Conclusion; Appendix; Assumptions; References; 4 Bayesian Nonparametric Spatially Smoothed Density Estimation; 4.1 Introduction
4.2 The Predictive Model4.2.1 Markov Chain Monte Carlo; 4.2.2 Censored Data; 4.2.3 Direct Estimation and a Permutation Test p-Value; 4.3 Examples; 4.3.1 IgG Distribution Evolving with Age; 4.3.2 Time to Infection in Amphibian Populations; 4.3.3 Simulated Data; 4.4 Conclusion; References; Part II Wavelet-Based Approach for Complex Data; 5 Mammogram Diagnostics Using Robust Wavelet-Based Estimator of Hurst Exponent; 5.1 Introduction; 5.2 Background; 5.2.1 Non-decimated Wavelet Transforms; 5.2.2 The fBm: Wavelet Coefficients and Spectra; 5.3 General Trimean Estimators