Statistical Learning With Sparsity The Lasso And Generalizations Chapman Hall Crc Monographs On Statistics Applied Probability PDF EPUB Download
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Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data. Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of l1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso. In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.
Over the past fifty years, wildlife science has become increasingly quantitative. But to wildlife scientists, many of whom have not been formally trained as biometricians, computer modelers, or mathematicians, the wide array of available techniques for analyzing wildlife populations and habitats can be overwhelming. This practical book aims to help students and professionals alike understand how to use quantitative methods to inform their work in the field. Covering the most widely used contemporary approaches to the analysis of wildlife populations and habitats, Quantitative Analyses in Wildlife Science is divided into five broad areas: • general statistical methods • demographic estimation • dynamic process modeling • analysis of spatially based data on animals and resources • numerical methods Addressing a variety of topics, from population estimation and growth trend predictions to the study of migration patterns, this book presents fresh data on such pressing issues as sustainable take, control of invasives, and species reintroduction. Authored by leading researchers in wildlife science, each chapter considers the structure of data in relation to a particular analytical technique, as well as the structure of variation in those data. Providing conceptual and quantitative overviews of modern analytical methods, the techniques covered in this book also apply to conservation research and wildlife policy. Whether a quick refresher or a comprehensive introduction is called for, Quantitative Analyses in Wildlife Science is an indispensable addition to every wildlife professional's bookshelf. Contributors: William M. Block, Leonard A. Brennan, Stephen T. Buckland, Christopher C. Chizinski, Evan C. Cooch, Raymond J. Davis, Stephen J. DeMaso, Randy W. DeYoung, Jane Elith, Joseph J. Fontane, Julie A. Heinrichs, Mevin B. Hooten, Julianna M. A. Jenkins, Zachary S. Laden, Damon B. Lesmeister, Daniel Linden, Jeffrey J. Lusk, Bruce G. Marcot, David L. Miller, Michael L. Morrison, Eric Rexstad, Jamie S. Sanderlin, Joseph P. Sands, Erica F. Stuber, Chris Sutherland, Andrew N. Tri, David B. Wester, Gary C. White, Christopher K. Williams, Damon L. Williford