Least-angle regression
In statistics, least-angle regression (LARS) is an algorithm for fitting linear regression models to high-dimensional data, developed by Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani. Suppose we expect a response variable to be determined by a linear combination of a subset of potential covariates. Then the LARS algorithm provides a means of producing an estimate of which variables to include, as well as their coefficients.
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Biological network inferenceBradley EfronLARSLasso (statistics)Least-Angle RegressionLeast Angle RegressionLeast angle regressionLeast squaresLinear regressionList of statistics articlesMlpackOutline of machine learningRegularized least squaresSparse approximationSparse dictionary learningStepwise regression
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Least-angle regression
In statistics, least-angle regression (LARS) is an algorithm for fitting linear regression models to high-dimensional data, developed by Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani. Suppose we expect a response variable to be determined by a linear combination of a subset of potential covariates. Then the LARS algorithm provides a means of producing an estimate of which variables to include, as well as their coefficients.
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In statistics, least-angle reg ...... orrelations with the residual.
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In statistics, least-angle reg ...... as well as their coefficients.
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Least-angle regression
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