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Extended BIC for linear regression models with diverging number of relevant features and high or ultra-high feature spaces
Diverging number of parameters Feature selection
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2011/7/19
In many conventional scientific investigations with high or ultra-high dimensional feature spaces, the relevant features, though sparse, are large in number compared with classical statistical problem...
Stochastic Search for Semiparametric Linear Regression Models
Stochastic Search Semiparametric Linear Regression Models
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2011/7/6
This paper introduces and analyzes a stochastic search method for parameter estimation in linear regression models in the spirit of Beran and Millar (1987).
The random design setting for linear regression concerns estimators based on a random sample of covariate/response pairs.
Adaptive and Optimal Online Linear Regression on L1-balls
online linear regression indi-vidual sequences Adaptive Optimal
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2011/6/20
We consider the problem of online linear regression on indi-
vidual sequences. The goal in this paper is for the forecaster to output
sequential predictions which are, after T time rounds, almost as...
Varying-coefficient functional linear regression
asymptotics eigenfunctions functional data analysis local polynomial smoothing longitudinal data varying-coeffi cient models
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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
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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
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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...
Exact block-wise optimization in group lasso for linear regression
Block coordinate descent convex optimization group LASSO sparse group LASSO
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2010/10/19
The group lasso is a penalized regression method, used in regression problems where the covariates are partitioned into groups to promote sparsity at the group level. Existing methods for finding the ...
Robust linear regression through PAC-Bayesian truncation
Linear regression Generalization error Shrinkage
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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...
Bayesian predictive densities for linear regression models under alpha-divergence loss：some results and open problems
shrinkage prior Bayesian predictive density alpha-divergence Stein effect
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2010/3/10
This paper considers estimation of the predictive density for
a normal linear model with unknown variance under -divergence loss for
−1 1. We first give a general canonical form for the...
Admissibility of the usual confidence interval in linear regression
admissibility compromise decision theory confidence interval decisiontheory
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2010/3/9
Consider a linear regression model with independent and identically normally distributed
random errors. Suppose that the parameter of interest is a specified linear
combination of the regression par...
Post-L1-Penalized Estimators in High-Dimensional Linear Regression Models
Post-L1-Penalized Estimators High-Dimensional Linear Regression Models
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2010/3/9
In this paper we study the post-penalized estimator which applies ordinary,
unpenalized linear regression to the model selected by the first step penalized estimators,
typically the LASSO. It is wel...
Workforce analysis using data mining and linear regression to understand HIV/AIDS prevalence patterns
workforce analysis data mining linear regression HIV/AIDS prevalence patterns
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2009/9/27
Increased nurse and physician density are associated with improved health outcomes, suggesting that countries aiming to attain the MDGs related to HIV/AIDS would do well to invest in their health work...
Test for differences between M-estimates of non-linear regression model
M-estimates non-linear regression model
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2009/9/23
Test for differences between M-estimates of non-linear regression model。
M-estimation for linear regression with infinite variance
M-estimation linear regression infinite variance
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2009/9/22
The limiting behavior of M-estimates for a Iinear model
when the regressors and/or errors have heavy tailed distributions is
given. By hermy toil we mean that the distribution is in the domain of
a...