Input selection and shrinkage in multiresponse linear regression

DOI10.1016/j.csda.2007.01.025zbMath1452.62513OpenAlexW2161082510MaRDI QIDQ1020828

Timo Similä, J. Tikka

Publication date: 2 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.104.6019

zbMATH Keywords

convex optimizationsubset selectionvariable selectioncone programmingmultivariate regressionconstrained regression

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

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Uses Software

• SeDuMi

Cites Work

• Component selection and smoothing in multivariate nonparametric regression
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• Adaptive multivariate ridge regression
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• Second-order cone programming
• Least angle regression. (With discussion)
• Using SeDuMi 1.02, A Matlab toolbox for optimization over symmetric cones
• A new approach to variable selection in least squares problems
• Bayes Model Averaging with Selection of Regressors
• Predicting Multivariate Response in Linear Regression Model
• Model Selection for Multivariate Regression in Small Samples
• Sparse solutions to linear inverse problems with multiple measurement vectors
• A sparse signal reconstruction perspective for source localization with sensor arrays
• Model Selection and Estimation in Regression with Grouped Variables
• Dimensionality reduction approach to multivariate prediction
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