This paper considers the local linear regression estimators for partially linear model with censored data. Which have some nice large-sample behaviors and are easy to implement. By many simulation runs, the author als...This paper considers the local linear regression estimators for partially linear model with censored data. Which have some nice large-sample behaviors and are easy to implement. By many simulation runs, the author also found that the estimators show remarkable in the small sample case yet.展开更多
Partially linear varying coefficient model is a generalization of partially linear model and varying coefficient model and is frequently used in statistical modeling. In this paper, we construct estimators of the para...Partially linear varying coefficient model is a generalization of partially linear model and varying coefficient model and is frequently used in statistical modeling. In this paper, we construct estimators of the parametric and nonparametric components by Profile least-squares procedure which is based on local linear smoothing. The resulting estimators are shown to be asymptotically normal with heteroscedastic error.展开更多
We propose a new functional single index model, which called dynamic single-index model for functional data, or DSIM, to efficiently perform non-linear and dynamic relationships between functional predictor and functi...We propose a new functional single index model, which called dynamic single-index model for functional data, or DSIM, to efficiently perform non-linear and dynamic relationships between functional predictor and functional response. The proposed model naturally allows for some curvature not captured by the ordinary functional linear model. By using the proposed two-step estimating algorithm, we develop the estimates for both the link function and the regression coefficient function, and then provide predictions of new response trajectories. Besides the asymptotic properties for the estimates of the unknown functions, we also establish the consistency of the predictions of new response trajectories under mild conditions. Finally, we show through extensive simulation studies and a real data example that the proposed DSIM can highly outperform existed functional regression methods in most settings.展开更多
文摘This paper considers the local linear regression estimators for partially linear model with censored data. Which have some nice large-sample behaviors and are easy to implement. By many simulation runs, the author also found that the estimators show remarkable in the small sample case yet.
基金the National Natural Science Foundation of China (No.10431010)
文摘Partially linear varying coefficient model is a generalization of partially linear model and varying coefficient model and is frequently used in statistical modeling. In this paper, we construct estimators of the parametric and nonparametric components by Profile least-squares procedure which is based on local linear smoothing. The resulting estimators are shown to be asymptotically normal with heteroscedastic error.
基金supported by National Natural Science Foundation of China (Grant No. 11271080)
文摘We propose a new functional single index model, which called dynamic single-index model for functional data, or DSIM, to efficiently perform non-linear and dynamic relationships between functional predictor and functional response. The proposed model naturally allows for some curvature not captured by the ordinary functional linear model. By using the proposed two-step estimating algorithm, we develop the estimates for both the link function and the regression coefficient function, and then provide predictions of new response trajectories. Besides the asymptotic properties for the estimates of the unknown functions, we also establish the consistency of the predictions of new response trajectories under mild conditions. Finally, we show through extensive simulation studies and a real data example that the proposed DSIM can highly outperform existed functional regression methods in most settings.
