The identification of within-subject dependence is important for constructing efficient estimation in longitudinal data models.In this paper,we proposed a flexible way to study this dependence by using nonparametric r...The identification of within-subject dependence is important for constructing efficient estimation in longitudinal data models.In this paper,we proposed a flexible way to study this dependence by using nonparametric regression models.Specifically,we considered the estimation of varying coefficient longitudinal data model with non-stationary varying coefficient autoregressive error process over observational time quantum.Based on spline approximation and local polynomial techniques,we proposed a two-stage nonparametric estimation for unknown functional coefficients and didn’t not drop any observations in a hybrid least square loss framework.Moreover,we showed that the estimated coefficient functions are asymptotically normal and derived the asymptotic biases and variances accordingly.Monte Carlo studies and two real applications were conducted for illustrating the performance of our proposed methods.展开更多
In this paper, we propose a class of varying coefficient seemingly unrelated regression models, in which the errors are correlated across the equations. By applying the series approximation and taking the contemporane...In this paper, we propose a class of varying coefficient seemingly unrelated regression models, in which the errors are correlated across the equations. By applying the series approximation and taking the contemporaneous correlations into account, we propose an efficient generalized least squares series estimation for the unknown coefficient functions. The consistency and asymptotic normality of the resulting estimators are established. In comparison with the ordinary/east squares ones, the proposed estimators are more efficient with smaller asymptotical variances. Some simulgtlon＇studies and a real application are presented to demonstrate the finite sample performance of the proposed methods. In addition, based on a B-spline approximation, we deduce the asymptotic bias and variance of the proposed estimators.展开更多
基金supported by MOE(Ministry of Education in China),Project of Humanities and Social Sciences(No.15YJA910004)Sponsored by K.C.Wong Magna Fund in Ningbo University+1 种基金supported by the National Social Science Foundation of China(No.17BTJ025)the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science(East China Normal University),Ministry of Education(No.KLATASDS1802)
文摘The identification of within-subject dependence is important for constructing efficient estimation in longitudinal data models.In this paper,we proposed a flexible way to study this dependence by using nonparametric regression models.Specifically,we considered the estimation of varying coefficient longitudinal data model with non-stationary varying coefficient autoregressive error process over observational time quantum.Based on spline approximation and local polynomial techniques,we proposed a two-stage nonparametric estimation for unknown functional coefficients and didn’t not drop any observations in a hybrid least square loss framework.Moreover,we showed that the estimated coefficient functions are asymptotically normal and derived the asymptotic biases and variances accordingly.Monte Carlo studies and two real applications were conducted for illustrating the performance of our proposed methods.
基金Xu’s research was supported by Key Academic Project from Bureau of Statistics of Zhejiang Province(201325)Research Project of the National Statistics(2013LY119)+1 种基金Bai’s work was partially supported by National Natural Science Funds for Young Scholar(No.11001162)Shanghai University of Finance and Economics through Project 211 Phase IV and Shanghai Leading Academic Discipline Project(No.B804)
文摘In this paper, we propose a class of varying coefficient seemingly unrelated regression models, in which the errors are correlated across the equations. By applying the series approximation and taking the contemporaneous correlations into account, we propose an efficient generalized least squares series estimation for the unknown coefficient functions. The consistency and asymptotic normality of the resulting estimators are established. In comparison with the ordinary/east squares ones, the proposed estimators are more efficient with smaller asymptotical variances. Some simulgtlon＇studies and a real application are presented to demonstrate the finite sample performance of the proposed methods. In addition, based on a B-spline approximation, we deduce the asymptotic bias and variance of the proposed estimators.
