We propose inference tools for least angle regression and the lasso, from the joint distribution of suitably normalized spacings of the LARS algorithm. From this we extend the results of the asymptotic null distribution of the “covariance test ” of Lockhart et al. (2013). But we go much further, deriving exact finite sample results for a new asymptotically equivalent procedure called the “spacing test”. This provides exact conditional tests at any step of the LAR algorithm as well as “selection intervals ” for the appropriate true underlying regression parameter. Remarkably, these tests and intervals account correctly for the adaptive selection done by LARS
In this paper we consider the problem of building a linear prediction model when the number of candi...
The lasso is a popular tool that can be used for variable selection and esti- mation, however, class...
As lasso regression has grown exceedingly popular as a tool for coping with variable selection in hi...
The purpose of model selection algorithms such as All Subsets, Forward Selection, and Backward Elimi...
ABSTRACT. Recent advances in Post-Selection Inference have shown that conditional testing is relevan...
22 pages, 8 figuresInternational audienceRecent advances in Post-Selection Inference have shown that...
We investigate multiple testing and variable selection using the Least Angle Regression (LARS) algor...
39 pages, 7 figuresIn this article we investigate the outcomes of the standard Least Angle Regressio...
In variable selection problems, when the number of candidate covariates is relatively large, the "tw...
Algorithms for simultaneous shrinkage and selection in regression and classification provide attract...
Recent developments by Lee et al. (2014) in post selection inference for the Lasso are adapted to th...
The least angle regression selection (LARS) algorithms that use the classical sample means, variance...
The issue of model selection has drawn the attention of both applied and theoretical statisticians f...
We develop a framework for post model selection inference, via marginal screening, in linear regress...
We derive new theoretical results on the properties of the adaptive least absolute shrink-age and se...
In this paper we consider the problem of building a linear prediction model when the number of candi...
The lasso is a popular tool that can be used for variable selection and esti- mation, however, class...
As lasso regression has grown exceedingly popular as a tool for coping with variable selection in hi...
The purpose of model selection algorithms such as All Subsets, Forward Selection, and Backward Elimi...
ABSTRACT. Recent advances in Post-Selection Inference have shown that conditional testing is relevan...
22 pages, 8 figuresInternational audienceRecent advances in Post-Selection Inference have shown that...
We investigate multiple testing and variable selection using the Least Angle Regression (LARS) algor...
39 pages, 7 figuresIn this article we investigate the outcomes of the standard Least Angle Regressio...
In variable selection problems, when the number of candidate covariates is relatively large, the "tw...
Algorithms for simultaneous shrinkage and selection in regression and classification provide attract...
Recent developments by Lee et al. (2014) in post selection inference for the Lasso are adapted to th...
The least angle regression selection (LARS) algorithms that use the classical sample means, variance...
The issue of model selection has drawn the attention of both applied and theoretical statisticians f...
We develop a framework for post model selection inference, via marginal screening, in linear regress...
We derive new theoretical results on the properties of the adaptive least absolute shrink-age and se...
In this paper we consider the problem of building a linear prediction model when the number of candi...
The lasso is a popular tool that can be used for variable selection and esti- mation, however, class...
As lasso regression has grown exceedingly popular as a tool for coping with variable selection in hi...