Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine

Course Description: This course provides an introduction to computational statistics, with emphasis on statistical methods and software for addressing high-dimensional inference problems that arise in current biological and medical research. The following topics are surveyed in terms of both statistical methodology and software implementation: numerical and graphical summaries of data; loss-based estimation with cross-validation: regression, classification, maximum likelihood estimation, density estimation, variable selection; smoothing: robust local regression, kernel density estimation,
splines; EM algorithm; Markov chain Monte-Carlo procedures; multiple hypothesis testing; cluster analysis;
hidden Markov processes; resampling: cross-validation, bootstrap; the design of in silico experiments.

The course also discusses statistical computing resources, with emphasis on the R language and environment www.r-project.org. The statistical and computational methods are motivated by and illustrated on data structures that arise in current high-dimensional inference problems in biology and medicine.