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2003
In sparse regression, the goal is to obtain an estimate of the regression coefficients in which several of them are set exactly to zero. Sparseness is a desirable feature in regression problems, for several reasons. For example, in linear regression, sparse models are interpretable, that is, we find which variables are relevant; in kernel-based methods,
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In sparse regression, the goal is to obtain an estimate of the regression coefficients in which several of them are set exactly to zero. Sparseness is a desirable feature in regression problems, for several reasons. For example, in linear regression, sparse models are interpretable, that is, we find which variables are relevant; in kernel-based methods,
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Sparse Sliced Inverse Regression
Technometrics, 2006Sliced inverse regression (SIR) is an innovative and effective method for dimension reduction and data visualization of high-dimensional problems. It replaces the original variables with low-dimensional linear combinations of predictors without any loss of regression information and without the need to prespecify a model or an error distribution ...
Lexin Li, Christopher J Nachtsheim
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