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A Dirty Model for Multiple Sparse Regression
Terms—Multi-task Learning High-dimensional Statistics
2011/7/7
Sparse linear regression -- finding an unknown vector from linear measurements -- is now known to be possible with fewer samples than variables, via methods like the LASSO. We consider the multiple sp...
Classical regression analysis relates the expectation of a response variable to a linear combination of explanatory variables. In this article, we propose a covariance regression model that parameteri...
Varying-coefficient functional linear regression
asymptotics eigenfunctions functional data analysis local polynomial smoothing longitudinal data varying-coeffi cient models
2011/3/24
Functional linear regression analysis aims to model regression relations which include a functional predictor. The analog of the regression parameter vector or matrix in conventional multivariate or m...
Functional linear regression via canonical analysis
canonical components covariance operator functional data analysis functional linear model longitudinal data parameter function stochastic process
2011/3/24
We study regression models for the situation where both dependent and independent variables are square-integrable stochastic processes. Questions concerning the definition and existence of the corresp...
The Loss Rank Criterion for Variable Selection in Linear Regression Analysis
Model selection lasso loss rank principle shrinkage parameter variable se-lection
2010/11/9
Lasso and other regularization procedures are attractive methods for variable selection, subject to a proper choice of shrinkage parameter. Given a set of potential subsets produced by a regularizatio...
Robust linear regression through PAC-Bayesian truncation
Linear regression Generalization error Shrinkage
2010/10/14
We consider the problem of predicting as well as the best linear combination of d given functions in least squares regression under $L^\infty$ constraints on the linear combination. When the input dis...
Sensitivity Analysis to Select the Most Influential Risk Factors in a Logistic Regression Model
Risk Factors Logistic Regression Model
2009/9/3
The traditional variable selection methods for survival data depend on iteration procedures, and control of this process assumes tuning parameters that are problematic and time consuming, especially i...