Lasso is an application server and server management interface used to develop internet applications and is a general-purpose, high-level programmingâ€¦Â (More)

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Highly Cited

2011

Highly Cited

2011

This is equivalent to minimizing the sum of squares with a constraint of the form Î£ |Î²j| s. It is similar to ridge regressionâ€¦Â (More)

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Highly Cited

2010

Highly Cited

2010

We consider the group lasso penalty for the linear model. We note that the standard algorithm for solving the problem assumesâ€¦Â (More)

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Highly Cited

2009

Highly Cited

2009

We propose a new penalty function which, when used as regularization for empirical risk minimization procedures, leads to sparseâ€¦Â (More)

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Highly Cited

2007

Highly Cited

2007

The group lasso is an extension of the lasso to do variable selection on (predefined) groups of variables in linear regressionâ€¦Â (More)

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Highly Cited

2007

Highly Cited

2007

We exhibit an approximate equivalence between the Lasso es-timator and Dantzig selector. For both methods we derive parallelâ€¦Â (More)

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Highly Cited

2006

Highly Cited

2006

Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in sciences and social sciencesâ€¦Â (More)

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Highly Cited

2006

Highly Cited

2006

- Hui Zou
- 2006

The lasso is a popular technique for simultaneous estimation and variable selection. Lasso variable selection has been shown toâ€¦Â (More)

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Highly Cited

2005

Highly Cited

2005

The Lasso estimate for linear regression parameters can be interpreted as a Bayesian posterior mode estimate when the regressionâ€¦Â (More)

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Highly Cited

2004

Highly Cited

2004

The lasso penalizes a least squares regression by the sum of the absolute values (L1-norm) of the coefficients. The form of thisâ€¦Â (More)

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Highly Cited

1999

Highly Cited

1999

Proposed by Tibshirani (1996), the LASSO (least absolute shrinkage and selection operator) estimates a vector of regressionâ€¦Â (More)

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