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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...
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...
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...
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 ...
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...
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...
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...
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...
Test for differences between M-estimates of non-linear regression model。
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...
We establish new almost sure properties for powers of weighted martingale transbrms. It allows us to deduce usefuI asymptotic results for cumulative prediction and estimation errors associated with...
We describe and investigate new tests for testing the validity of a semiparametric random-design linear regression model. The tests were introduced in Inglot and Ledwina (2006a, b). We repeat here ...
Corrected Likelihood Ratio Tests in Class of Symmetric Linear Regression Models
The paper considers functional linear regression, where scalar responses Y1, . . . ,Yn are modeled in dependence of random functions X1, . . . ,Xn. We propose a smoothing splines estimator for the f...

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