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Least angle regression
, 2004
"... 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 s ..."
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Cited by 1326 (37 self)
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to select a parsimonious set for the efficient prediction of a response variable. Least Angle Regression (LARS), a new model selection algorithm, is a useful and less greedy version of traditional forward selection methods. Three main properties are derived: (1) A simple modification of the LARS algorithm
Least Angle Regression
 Annals of Statistics
, 2002
"... 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 ..."
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Cited by 3 (0 self)
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to select a parsimonious set for the eificient pre diction of a response variable. Least Angle Regression (" LARS"), a new model selection algorithm, is a useful and less greedy version of traditional forward selection methods.
Shrinkage methods: Least angles regression and LassoOutline Least Angle regression
, 2009
"... Conclusion The problem and the notation Nagarajan Natarajan, vishvAs vAsuki Shrinkage methods: Least angles regression and LassoOutline ..."
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Conclusion The problem and the notation Nagarajan Natarajan, vishvAs vAsuki Shrinkage methods: Least angles regression and LassoOutline
Discussion of Least Angle Regression
 Annals of Statistics
, 2004
"... this paper is an important contribution to statistical computing. Predictive Performance The authors say little about predictive performance issues. In our work, however, the relative outofsample predictive performance of LARS, Lasso, and forwardstagewise (and variants thereof) takes center stage ..."
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generally, largerscale applications will want to use # to select the degree of shrinkage. Table 2 presents a reanalysis of the same three datasets but now considering five different models: least squares, LARS using crossvalidation to select the coefficients, LARS using # to select the coeffi...
Least Angle Regression for Time Series Forecasting with Many Predictors
"... Least angle regression for time series forecasting with ..."
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Cited by 6 (0 self)
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Least angle regression for time series forecasting with
DISCUSSION ON THE PAPER ”LEAST ANGLE REGRESSION”
"... The issue of model selection has drawn the attention of both applied and theoretical statisticians for a long time. Indeed, there has been an enormous range of contribution in model selection proposal, which includes the work by Akaike [1], Mallows [7], Foster and George [5], Birge ́ and Massart [3] ..."
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estimator. Consider the linear regression model where we observe y with some random noise ε, with orthogonal design assumptions: y = Xβ + ε. Using the softthresholding form of the estimator, we can write it, in an equivalent way, as the minimum of an ordinary leastsquares and a l1 penalty over
SPLUS and R package for least angle regression
 IN PROCEEDINGS OF THE AMERICAN STATISTICAL ASSOCIATION, STATISTICAL COMPUTING SECTION [CDROM
, 2006
"... Least Angle Regression is a promising technique for variable selection applications, offering a nice alternative to stepwise regression. It provides an explanation for the similar behavior of Lasso (L1penalized regression) and forward stagewise regression, and provides a fast implementation of bot ..."
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Cited by 2 (2 self)
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Least Angle Regression is a promising technique for variable selection applications, offering a nice alternative to stepwise regression. It provides an explanation for the similar behavior of Lasso (L1penalized regression) and forward stagewise regression, and provides a fast implementation
Discussion of ‘Least Angle Regression’ by Efron, et al
 Annals of Statistics
, 2004
"... 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 ..."
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Cited by 4 (1 self)
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for stagewise regression and the new least angle regression. As such this paper is an important contribution to statistical computing. 1. Predictive performance. The authors say little about predictive performance issues. In our work, however, the relative outofsample predictive performance of LARS, Lasso
© Institute of Mathematical Statistics, 2004 LEAST 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 s ..."
Abstract
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to select a parsimonious set for the efficient prediction of a response variable. Least Angle Regression (LARS), a new model selection algorithm, is a useful and less greedy version of traditional forward selection methods. Three main properties are derived: (1) A simple modification of the LARS algorithm
POWER OF THE SPACING TEST FOR LEASTANGLE REGRESSION
"... ABSTRACT. Recent advances in PostSelection Inference have shown that conditional testing is relevant and tractable in highdimensions. In the Gaussian linear model, further works have derived unconditional test statistics such as the KacRice Pivot for general penalized problems. In order to test t ..."
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the global null, a prominent offspring of this breakthrough is the spacing test that accounts the relative separation between the first two knots of the celebrated leastangle regression (LARS) algorithm. However, no results have been shown regarding the distribution of these test statistics under
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