In this paper, we investigate the estimation of semi-varying coefficient models when the nonlinear covariates are prone to measurement error. With the help of validation sampling, we propose two estimators of the para...In this paper, we investigate the estimation of semi-varying coefficient models when the nonlinear covariates are prone to measurement error. With the help of validation sampling, we propose two estimators of the parameter and the coefficient functions by combining dimension reduction and the profile likelihood methods without any error structure equation specification or error distribution assumption. We establish the asymptotic normality of proposed estimators for both the parametric and nonparametric parts and show that the proposed estimators achieves the best convergence rate. Data-driven bandwidth selection methods are also discussed. Simulations are conducted to evaluate the finite sample property of the estimation methods proposed.展开更多
基金Supported by the National Natural Science Foundation of China(No.10871072,11171112 and 11101114)the Scientific Research Fund of Zhejiang Provincial Education Department(Grant No.Y201121276)the Doctoral Fund of Ministry of Education of China(200900076110001)
文摘In this paper, we investigate the estimation of semi-varying coefficient models when the nonlinear covariates are prone to measurement error. With the help of validation sampling, we propose two estimators of the parameter and the coefficient functions by combining dimension reduction and the profile likelihood methods without any error structure equation specification or error distribution assumption. We establish the asymptotic normality of proposed estimators for both the parametric and nonparametric parts and show that the proposed estimators achieves the best convergence rate. Data-driven bandwidth selection methods are also discussed. Simulations are conducted to evaluate the finite sample property of the estimation methods proposed.
基金supported by the Science and Technology Research Program of Chongqing Education Commission(Grant No.KJZD-M202100801)the Fifth Batch of Excellent Talent Support Program of Chongqing Colleges and University(Grant No.68021900601)+4 种基金the Natural Science Foundation of CQ CSTC(Grant No.cstc.2018jcyjA2073)the Program for the Chongqing Statistics Postgraduate Supervisor Team(Grant No.yds183002)the Chongqing Social Science Plan Project(Grant No.2019WT59)the Open Project from Chongqing Key Laboratory of Social Economy and Applied Statistics(Grant No.KFJJ2018066)the Mathematic and Statistics Team from Chongqing Technology and Business University(Grant No.ZDPTTD201906).
基金Supported by the Science and the Technology Program for Guangdong Province(2012B010100044)the Science and Technological Program for Dongguan Higher Education,and the Science and Research Institutions(2012108102031)
