edited by Heping Zhang (Yale University School of Medicine, USA) & Jian Huang (University of Iowa, USA)

This book encompasses a wide range of important topics. The articles cover the following areas: asymptotic theory and inference, biostatistics, economics and finance, statistical computing and Bayesian statistics, and statistical genetics. Specifically, the issues that are studied include large deviation, deviation inequalities, local sensitivity of model misspecification in likelihood inference, empirical likelihood confidence intervals, uniform convergence rates in density estimation, randomized designs in clinical trials, MCMC and EM algorithms, approximation of p-values in multipoint linkage analysis, use of mixture models in genetic studies, and design and analysis of quantitative traits.

• An Interview with Professor Yaoting Zhang (Q-W Yao & Z-H Li)
• A Monte Carlo Gap Test in Computing HPD Regions (M-H Chen et al.)
• An Example of Algorithm Mining: Covariance Adjustment to Accelerate EM and Gibbs (C-H Liu)
• Empirical Likelihood Confidence Intervals for the Difference of Two Quantiles of a Population (Y-S Qin & Y-H Wu)
• Sharing Catastrophe Risk Under Model Uncertainty (X-D Zhu)
• Some Recent Advances on Response-Adaptive Randomized Designs (F-F Hu)
• A Childhood Epidemic Model with Birthrate-Dependent Transmission (Y-C Xia)
• Structure Mixture Regression Models (H-T Zhu & H-P Zhang)
• and other papers