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
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