The Experts below are selected from a list of 247506 Experts worldwide ranked by ideXlab platform
Wang Shiqing - One of the best experts on this subject based on the ideXlab platform.
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the minimax admissibility Estimates of multivariate regression coefficient in the restricted growth curve model under quadratic loss function
Journal of Nanyang Normal University, 2006Co-Authors: Wang ShiqingAbstract:In this paper,we consider the minimax admissibility Estimates of the restricted multivariate regression coefficient under quadratic loss function.The necessary and sufficient condition are given for a Linear Estimate MYN(MYN+C)to be minimax admissible in the class of some homogeneous(non-homogenous)Linear Estimates,and a minimax admissible Estimate is given.
Linglong Kong - One of the best experts on this subject based on the ideXlab platform.
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multivariate varying coefficient model for functional responses
Annals of Statistics, 2012Co-Authors: Runze Li, Linglong KongAbstract:Motivated by recent work studying massive imaging data in the neuroimaging literature, we propose multivariate varying coefficient models (MVCM) for modeling the relation between multiple functional responses and a set of covariates. We develop several statistical inference procedures for MVCM and systematically study their theoretical properties. We first establish the weak convergence of the local Linear Estimate of coefficient functions, as well as its asymptotic bias and variance, and then we derive asymptotic bias and mean integrated squared error of smoothed individual functions and their uniform convergence rate. We establish the uniform convergence rate of the Estimated covariance function of the individual functions and its associated eigenvalue and eigenfunctions. We propose a global test for Linear hypotheses of varying coefficient functions, and derive its asymptotic distribution under the null hypothesis. We also propose a simultaneous confidence band for each individual effect curve. We conduct Monte Carlo simulation to examine the finite-sample performance of the proposed procedures. We apply MVCM to investigate the development of white matter diffusivities along the genu tract of the corpus callosum in a clinical study of neurodevelopment.
Runze Li - One of the best experts on this subject based on the ideXlab platform.
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multivariate varying coefficient model for functional responses
Annals of Statistics, 2012Co-Authors: Runze Li, Linglong KongAbstract:Motivated by recent work studying massive imaging data in the neuroimaging literature, we propose multivariate varying coefficient models (MVCM) for modeling the relation between multiple functional responses and a set of covariates. We develop several statistical inference procedures for MVCM and systematically study their theoretical properties. We first establish the weak convergence of the local Linear Estimate of coefficient functions, as well as its asymptotic bias and variance, and then we derive asymptotic bias and mean integrated squared error of smoothed individual functions and their uniform convergence rate. We establish the uniform convergence rate of the Estimated covariance function of the individual functions and its associated eigenvalue and eigenfunctions. We propose a global test for Linear hypotheses of varying coefficient functions, and derive its asymptotic distribution under the null hypothesis. We also propose a simultaneous confidence band for each individual effect curve. We conduct Monte Carlo simulation to examine the finite-sample performance of the proposed procedures. We apply MVCM to investigate the development of white matter diffusivities along the genu tract of the corpus callosum in a clinical study of neurodevelopment.
Wang Cheng-ming - One of the best experts on this subject based on the ideXlab platform.
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Admissibility of Linear Estimate of Regression Coefficients in Growth Curve Model
Journal of Liuzhou Vocational & Technical College, 2005Co-Authors: Wang Cheng-mingAbstract:In this paper,we consider the admissibility of Linear Estimate of regression coefficients under a ma-trix loss function for a Growth Curve Model.The condition for a Linear Estimate of regression coefficients to beadmissible among the class of Linear Estimates is established.
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THE MINIMAX ADMISSIBILITY CHARACTERIZATION OF NONHOMOGENEOUS Linear EstimateS WITH RESPECT TO RESTRICTED REGRESSION COEFFICIENT UNDER MATRIX LOSS FUNCTION
Journal of Guangxi Normal University, 2001Co-Authors: Wang Cheng-mingAbstract:This paper considers the Minimax admissibility characterization of Linear Estimates with respect to restricted regression coefficient under a matrix loss function.The necessary and sufficient conditions are given for a Linear Estimate AY+c to be Minimax admissible in the class of nonhomogeneous Linear Estimates.
Ali Laksaci - One of the best experts on this subject based on the ideXlab platform.
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On the local Linear Estimate for functional regression: Uniform in bandwidth consistency
Communications in Statistics - Theory and Methods, 2018Co-Authors: Mohammed Attouch, Ali Laksaci, Fatima RafaaAbstract:We consider the problem of local Linear estimation of the regression function when the regressor is functional. The main result of this paper is to prove the strong convergence (with rates), unifor...
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Local Linear Estimate of the nonparametric robust regression in functional data
Statistics & Probability Letters, 2018Co-Authors: Faiza Belarbi, Souheyla Chemikh, Ali LaksaciAbstract:Abstract In this paper, we study the robust estimation of the functional local Linear regression model. The main results of this work are the establishment of the almost complete convergence as well as the asymptotic normality for the constructed estimator.
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Functional local Linear Estimate for functional relative-error regression
Journal of Statistical Theory and Practice, 2017Co-Authors: Abdelkader Chahad, Larbi Ait-hennani, Ali LaksaciAbstract:We present in this article a new estimator of the regression operator of a scalar response variable given a functional explanatory variable. The latter is constructed by minimizing the mean squared relative error of the local Linear regression operator. Our main result is the establishment of the almost complete consistency (pointwise and uniform) with rates of this estimator. A Monte Carlo study is carried out to evaluate the performance of this Estimate.