Contents Preface vii Principal Component Analysis (PCA) for High-Dimensional Data. PCA Is Dead. Long Live PCA Fan Yang, Kjell Doksum, and Kam-Wah Tsui 1 Solving a System of High-Dimensional Equations by MCMC Nozer D. Singpurwalla and Joshua Landon 11 A Slice Sampler for the Hierarchical Poisson/Gamma Random Field Model Jian Kang and Timothy D. Johnson 21 A New Penalized Quasi-Likelihood Approach for Estimating the Number of States in a Hidden Markov Model Annaliza McGillivray and Abbas Khalili 37 Efficient Adaptive Estimation Strategies in High-Dimensional Partially Linear Regression Models Xiaoli Gao and S. Ejaz Ahmed 61 Geometry and Properties of Generalized Ridge Regression in High Dimensions Hemant Ishwaran and J. Sunil Rao 81 Multiple Testing for High-Dimensional Data Guoqing Diao, Bret Hanlon, and Anand N. Vidyashankar 95 On Multiple Contrast Tests and Simultaneous Confidence Intervals in High-Dimensional Repeated Measures Designs Frank Konietschke, Yulia R. Gel, and Edgar Brunner 109 Data-Driven Smoothing Can Preserve Good Asymptotic Properties Zhouwang Yang, Huizhi Xie, and Xiaoming Huo 125 Variable Selection for Ultra-High-Dimensional Logistic Models Pang Du, Pan Wu, and Hua Liang 141 Shrinkage Estimation and Selection for a Logistic Regression Model Shakhawat Hossain and S. Ejaz Ahmed 159 Manifold Unfolding by Isometric Patch Alignment with an Application in Protein Structure Determination Pooyan Khajehpour Tadavani, Babak Alipanahi, and Ali Ghodsi 177 v
© Copyright 2024 ExpyDoc
