By Trevor Hastie, Visit Amazon's Robert Tibshirani Page, search results, Learn about Author Central, Robert Tibshirani, , Martin Wainwright
Discover New equipment for facing High-Dimensional Data
A sparse statistical version has just a small variety of nonzero parameters or weights; for this reason, it truly is a lot more uncomplicated to estimate and interpret than a dense version. Statistical studying with Sparsity: The Lasso and Generalizations offers tools that take advantage of sparsity to assist recuperate the underlying sign in a suite of data.
Top specialists during this quickly evolving box, the authors describe the lasso for linear regression and an easy coordinate descent set of rules for its computation. They speak about the applying of ℓ1 consequences to generalized linear types and help vector machines, conceal generalized consequences akin to the elastic web and crew lasso, and evaluate numerical tools for optimization. additionally they current statistical inference equipment for equipped (lasso) versions, together with the bootstrap, Bayesian equipment, and lately built techniques. furthermore, the publication examines matrix decomposition, sparse multivariate research, graphical types, and compressed sensing. It concludes with a survey of theoretical effects for the lasso.
In this age of huge facts, the variety of positive aspects measured on someone or item could be huge and may be better than the variety of observations. This publication exhibits how the sparsity assumption permits us to take on those difficulties and extract important and reproducible styles from titanic datasets. info analysts, machine scientists, and theorists will have fun with this thorough and up to date therapy of sparse statistical modeling.