In this paper,we consider parameter estimation,kink points testing and statistical inference for a longitudinal multi-kink expectile regression model with nonignorable dropout.In order to accommodate both within-subje...In this paper,we consider parameter estimation,kink points testing and statistical inference for a longitudinal multi-kink expectile regression model with nonignorable dropout.In order to accommodate both within-subject correlations and nonignorable dropout,the bias-corrected generalized estimating equations are constructed by combining the inverse probability weighting and quadratic inference function approaches.The estimators for the kink locations and regression coefficients are obtained by using the generalized method of moments.A selection procedure based on a modified BIC is applied to estimate the number of kink points.We theoreti-cally demonstrate the number selection consistency of kink points and the asymptotic normality of all estimators.A weighted cumulative sum type statistic is proposed to test the existence of kink effects at a given expectile,and its limiting distributions are derived under both the null and the local alternative hypotheses.Simulation studies show that the proposed estimators and test have desirable finite sample performance in both homoscedastic and heteroscedastic errors.An application to the Nation Growth,Lung and Health Study dataset is also presented.展开更多
Variable selection for varying coefficient models includes the separation of varying and constant effects,and the selection of variables with nonzero varying effects and those with nonzero constant effects.This paper ...Variable selection for varying coefficient models includes the separation of varying and constant effects,and the selection of variables with nonzero varying effects and those with nonzero constant effects.This paper proposes a unified variable selection approach called the double-penalized quadratic inference functions method for varying coefficient models of longitudinal data.The proposed method can not only separate varying coefficients and constant coefficients,but also estimate and select the nonzero varying coefficients and nonzero constant coefficients.It is suitable for variable selection of linear models,varying coefficient models,and partial linear varying coefficient models.Under regularity conditions,the proposed method is consistent in both separation and selection of varying coefficients and constant coefficients.The obtained estimators of varying coefficients possess the optimal convergence rate of non-parametric function estimation,and the estimators of nonzero constant coefficients are consistent and asymptotically normal.Finally,the authors investigate the finite sample performance of the proposed method through simulation studies and a real data analysis.The results show that the proposed method performs better than the existing competitor.展开更多
基金supported by the Fundamental Research Funds for the Central Universities and the National Natural Science Foundation of China[Grant Numbers 12271272 and 12001295].
文摘In this paper,we consider parameter estimation,kink points testing and statistical inference for a longitudinal multi-kink expectile regression model with nonignorable dropout.In order to accommodate both within-subject correlations and nonignorable dropout,the bias-corrected generalized estimating equations are constructed by combining the inverse probability weighting and quadratic inference function approaches.The estimators for the kink locations and regression coefficients are obtained by using the generalized method of moments.A selection procedure based on a modified BIC is applied to estimate the number of kink points.We theoreti-cally demonstrate the number selection consistency of kink points and the asymptotic normality of all estimators.A weighted cumulative sum type statistic is proposed to test the existence of kink effects at a given expectile,and its limiting distributions are derived under both the null and the local alternative hypotheses.Simulation studies show that the proposed estimators and test have desirable finite sample performance in both homoscedastic and heteroscedastic errors.An application to the Nation Growth,Lung and Health Study dataset is also presented.
基金supported in part by the National Science Foundation of China under Grant Nos.12071305and 71803001in part by the national social science foundation of China under Grant No.19BTJ014+1 种基金in part by the University Social Science Research Project of Anhui Province under Grant No.SK2020A0051in part by the Social Science Foundation of the Ministry of Education of China under Grant Nos.19YJCZH250 and 21YJAZH081。
文摘Variable selection for varying coefficient models includes the separation of varying and constant effects,and the selection of variables with nonzero varying effects and those with nonzero constant effects.This paper proposes a unified variable selection approach called the double-penalized quadratic inference functions method for varying coefficient models of longitudinal data.The proposed method can not only separate varying coefficients and constant coefficients,but also estimate and select the nonzero varying coefficients and nonzero constant coefficients.It is suitable for variable selection of linear models,varying coefficient models,and partial linear varying coefficient models.Under regularity conditions,the proposed method is consistent in both separation and selection of varying coefficients and constant coefficients.The obtained estimators of varying coefficients possess the optimal convergence rate of non-parametric function estimation,and the estimators of nonzero constant coefficients are consistent and asymptotically normal.Finally,the authors investigate the finite sample performance of the proposed method through simulation studies and a real data analysis.The results show that the proposed method performs better than the existing competitor.
