Sparse Graphical Modeling for High Dimensional Data

<p>This book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian Poisson multinomial and mixed data; unified treatments for covariate adjustments data integration and network comparison; unified treatments for missing data and heterogeneous data; efficient methods for joint estimation of multiple graphical models; effective methods of high-dimensional variable selection; and effective methods of high-dimensional inference. The methods possess an embarrassingly parallel structure in performing conditional independence tests and the computation can be significantly accelerated by running in parallel on a multi-core computer or a parallel architecture. This book is intended to serve researchers and scientists interested in high-dimensional statistics and graduate students in broad data science disciplines.</p><p>Key Features:</p><ul> <p> </p> <li>A general framework for learning sparse graphical models with conditional independence tests</li> <p> </p> <li>Complete treatments for different types of data Gaussian Poisson multinomial and mixed data</li> <p> </p> <li>Unified treatments for data integration network comparison and covariate adjustment</li> <p> </p> <li>Unified treatments for missing data and heterogeneous data</li> <p> </p> <li>Efficient methods for joint estimation of multiple graphical models</li> <p> </p> <li>Effective methods of high-dimensional variable selection</li> <li>Effective methods of high-dimensional inference</li> </ul>