Chen, Zehua - Department of Statistics and Applied Probability, National University of Singapore

• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• 5. Stratification to control prognostic 5.1. Stratification and randomization
• 6. Regression control for prognostic 6.1. Study of measurement of change
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• 4. Blocking to control prognostic 4.1. Complete randomized blocks de-
• 1. Overview of Clinical Trials 1.1. What are clinical trials?
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• Small-n-large-p and its challenges Screening-selection approach Adaptive screening-selection Numerical studies Conclusion R An Adaptive Screen-selection
• The Package migtlm: Multiple interval genetical trait loci mapping
• Mixture Generalized Linear Models for Multiple Interval Mapping of Quantitative Trait Loci
• ST4241: Design and Analysis of Clinical Trials (2010-2011 Semester I)
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• Estimation in Parametric Exercise 1 (#4.1). Show that the priors in the following cases are con-
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• A feature selection approach to case-control genome-wide association studies
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• Extended BIC for small-n-large-P sparse GLM By JIAHUA CHEN
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• 2. Parallel groups design and its data 2.1. Parallel groups design and ran-
• 3. Special cases of parallel groups 3.1. A general discussion
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• Publication List 1. J. Zhao and Z. Chen. A feature selection approach to case-control
• ST5215: Advanced Statistical Theory (I) Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory (I) Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• Web-based Supplementary Materials for A two-stage penalized logistic regression approach to
• Unbiased Estimation Exercise 1. Let X be a sample from P P and be a parameter. Show
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory (I) Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• ST5215: Advanced Statistical Theory Department of Statistics & Applied Probability
• Hypothesis Tests Exercise 1 (#6.2). Let X be a sample from a population P and consider
• Web-based Supplementary Materials for Selection Consistency of EBIC for GLIM with Non-canonical
• ST5224: Advanced Statistical Theory II Department of Statistics & Applied Probability
• ST5224: Advanced Statistical Theory II Department of Statistics & Applied Probability
• ST5224: Advanced Statistical Theory II Department of Statistics & Applied Probability
• ST5224: Advanced Statistical Theory II Department of Statistics & Applied Probability
• ST5224: Advanced Statistical Theory II Department of Statistics & Applied Probability
• ST5224: Advanced Statistical Theory II Department of Statistics & Applied Probability
• ST5224: Advanced Statistical Theory II Department of Statistics & Applied Probability
• ST5224: Advanced Statistical Theory II Department of Statistics & Applied Probability