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Download Free Inference for Sparse and Asymmetric Signals in High Dimensional Data with Applications to Statistical Genomics PDF by Min Zhang Full Book and published by . This book was released on 2005 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: We also developed a Bayesian model selection approach to select significant predictors from high dimensional linear models. Unlike classical approaches, this method incorporates the important information in high dimensional sparse data that, most of the predictors have no effect or the effects are too small to be detectable, and that positive effects and negative effects may not be symmetric. In addition, this Bayesian approach can naturally handle the important case that deals with missing data. The inference was carried out by using a Markov chain Monte Carlo (MCMC) sampling scheme. We evaluate the performance of this approach by simulation studies as well as applications to quantitative trait loci (QTL) mapping. This approach can be applied to both independent and clustered data.