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THE SOLUTION PATH OF THE GENERALIZED LASSO
by
Tibshirani, Ryan J.
, Taylor, Jonathan
in
62-XX
/ Algorithms
/ Approximation
/ Coordinate systems
/ Data smoothing
/ Degrees of freedom
/ Exact sciences and technology
/ General topics
/ Lagrange dual
/ LARS
/ Lasso
/ Least squares
/ Linear algebra
/ Mathematical vectors
/ Mathematics
/ Matrices
/ Numerical analysis
/ Numerical analysis. Scientific computation
/ Numerical linear algebra
/ Outliers
/ path algorithm
/ Polynomials
/ Probability and statistics
/ Regression analysis
/ Sciences and techniques of general use
/ Statistics
/ Studies
/ Unbiased estimators
2011
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THE SOLUTION PATH OF THE GENERALIZED LASSO
by
Tibshirani, Ryan J.
, Taylor, Jonathan
in
62-XX
/ Algorithms
/ Approximation
/ Coordinate systems
/ Data smoothing
/ Degrees of freedom
/ Exact sciences and technology
/ General topics
/ Lagrange dual
/ LARS
/ Lasso
/ Least squares
/ Linear algebra
/ Mathematical vectors
/ Mathematics
/ Matrices
/ Numerical analysis
/ Numerical analysis. Scientific computation
/ Numerical linear algebra
/ Outliers
/ path algorithm
/ Polynomials
/ Probability and statistics
/ Regression analysis
/ Sciences and techniques of general use
/ Statistics
/ Studies
/ Unbiased estimators
2011
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Do you wish to request the book?
THE SOLUTION PATH OF THE GENERALIZED LASSO
by
Tibshirani, Ryan J.
, Taylor, Jonathan
in
62-XX
/ Algorithms
/ Approximation
/ Coordinate systems
/ Data smoothing
/ Degrees of freedom
/ Exact sciences and technology
/ General topics
/ Lagrange dual
/ LARS
/ Lasso
/ Least squares
/ Linear algebra
/ Mathematical vectors
/ Mathematics
/ Matrices
/ Numerical analysis
/ Numerical analysis. Scientific computation
/ Numerical linear algebra
/ Outliers
/ path algorithm
/ Polynomials
/ Probability and statistics
/ Regression analysis
/ Sciences and techniques of general use
/ Statistics
/ Studies
/ Unbiased estimators
2011
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Journal Article
THE SOLUTION PATH OF THE GENERALIZED LASSO
2011
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Overview
We present a path algorithm for the generalized lasso problem. This problem penalizes the ℓ 1 norm of a matrix D times the coefficient vector, and has a wide range of applications, dictated by the choice of D. Our algorithm is based on solving the dual of the generalized lasso, which greatly facilitates computation of the path. For D = I (the usual lasso), we draw a connection between our approach and the well-known LARS algorithm. For an arbitrary D, we derive an unbiased estimate of the degrees of freedom of the generalized lasso fit. This estimate turns out to be quite intuitive in many applications.
Publisher
Institute of Mathematical Statistics,The Institute of Mathematical Statistics
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