To improve the prediction accuracy of semiparametric additive partial linear models(APLM) and the coverage probability of confidence intervals of the parameters of interest,we explore a focused information criterion f...To improve the prediction accuracy of semiparametric additive partial linear models(APLM) and the coverage probability of confidence intervals of the parameters of interest,we explore a focused information criterion for model selection among ALPM after we estimate the nonparametric functions by the polynomial spline smoothing,and introduce a general model average estimator.The major advantage of the proposed procedures is that iterative backfitting implementation is avoided,which thus results in gains in computational simplicity.The resulting estimators are shown to be asymptotically normal.A simulation study and a real data analysis are presented for illustrations.展开更多
In this paper,we consider composite quantile regression for partial functional linear regression model with polynomial spline approximation.Under some mild conditions,the convergence rates of the estimators and mean s...In this paper,we consider composite quantile regression for partial functional linear regression model with polynomial spline approximation.Under some mild conditions,the convergence rates of the estimators and mean squared prediction error,and asymptotic normality of parameter vector are obtained.Simulation studies demonstrate that the proposed new estimation method is robust and works much better than the least-squares based method when there are outliers in the dataset or the random error follows heavy-tailed distributions.Finally,we apply the proposed methodology to a spectroscopic data sets to illustrate its usefulness in practice.展开更多
基金supported by US National Science Foundation (Grant No.DMS-0806097)
文摘To improve the prediction accuracy of semiparametric additive partial linear models(APLM) and the coverage probability of confidence intervals of the parameters of interest,we explore a focused information criterion for model selection among ALPM after we estimate the nonparametric functions by the polynomial spline smoothing,and introduce a general model average estimator.The major advantage of the proposed procedures is that iterative backfitting implementation is avoided,which thus results in gains in computational simplicity.The resulting estimators are shown to be asymptotically normal.A simulation study and a real data analysis are presented for illustrations.
基金Supported by the National Natural Science Foundation of China(Grant Nos.11671096,11690013,11731011 and 12071267)the Natural Science Foundation of Shanxi Province,China(Grant No.201901D111279)。
文摘In this paper,we consider composite quantile regression for partial functional linear regression model with polynomial spline approximation.Under some mild conditions,the convergence rates of the estimators and mean squared prediction error,and asymptotic normality of parameter vector are obtained.Simulation studies demonstrate that the proposed new estimation method is robust and works much better than the least-squares based method when there are outliers in the dataset or the random error follows heavy-tailed distributions.Finally,we apply the proposed methodology to a spectroscopic data sets to illustrate its usefulness in practice.
