منابع مشابه
Robust groupwise least angle regression
Many regression problems exhibit a natural grouping among predictor variables. Examples are groups of dummy variables representing categorical variables, or present and lagged values of time series data. Since model selection in such cases typically aims for selecting groups of variables rather than individual covariates, an extension of the popular least angle regression (LARS) procedure to gr...
متن کاملLeast Angle Regression
The purpose of model selection algorithms such as All Subsets, Forward Selection, and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be applied. Typically we have available a large collection of possible covariates from which we hope to select a parsimonious set for the efficient prediction of a response variable. Least Angle Regres...
متن کاملDiscussion of “ Least Angle Regression ”
Being able to reliably, and automatically, select variables in linear regression models is a notoriously difficult problem. This research attacks this question head on, introducing not only a computationally efficient algorithm and method, LARS (and its derivatives), but at the same time introducing comprehensive theory explaining the intricate details of the procedure as well as theory to guid...
متن کاملDiscussion of Least Angle Regression
Algorithms for simultaneous shrinkage and selection in regression and classification provide attractive solutions to knotty old statistical challenges. Nevertheless, as far as we can tell, Tibshirani’s Lasso algorithm has had little impact on statistical practice. Two particular reasons for this may be the relative inefficiency of the original Lasso algorithm, and the relative complexity of mor...
متن کاملRobust linear least squares regression
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ژورنال
عنوان ژورنال: Computational Statistics & Data Analysis
سال: 2016
ISSN: 0167-9473
DOI: 10.1016/j.csda.2015.02.007