The frequentist model averaging(FMA)and the focus information criterion(FIC)under a local framework have been extensively studied in the likelihood and regression setting since the seminal work of Hjort and Claes kens...The frequentist model averaging(FMA)and the focus information criterion(FIC)under a local framework have been extensively studied in the likelihood and regression setting since the seminal work of Hjort and Claes kens in 2003.One inconvenience,however,of the existing works is that they usually require the involved criterion function to be twice differentiable which thus prevents a direct application to the case of quantile regression(QR).This as well as some other intrinsic merits of QR motivate us to study the FIC and FMA in a locally misspecified linear QR model.Specifically,we derive in this paper the explicit asymptotic risk expression for a general submodel-based QR estimator of a focus parameter.Then based on this asymptotic result,we develop the FIC and FMA in the current setting.Our theoretical development depends crucially on the convexity of the objective function,which makes possible to establish the asymptotics based on the existing convex stochastic process theory.Simulation studies are presented to illustrate the finite sample performance of the proposed method.The low birth weight data set is analyzed.展开更多
The main purpose of this paper is using capture-recapture data to estimate the population size when some covariate values are missing, possibly non-ignorable. Conditional likelihood method is adopted, with a sub-model...The main purpose of this paper is using capture-recapture data to estimate the population size when some covariate values are missing, possibly non-ignorable. Conditional likelihood method is adopted, with a sub-model describing various missing mechanisms. The derived estimate is proved to be asymptotically normal, and simulation studies via a version of EM algorithm show that it is approximately unbiased. The proposed method is applied to a real example, and the result is compared with previous ones.展开更多
基金This paper is supported by the National Natural Science Foundation of China(No.11771049).
文摘The frequentist model averaging(FMA)and the focus information criterion(FIC)under a local framework have been extensively studied in the likelihood and regression setting since the seminal work of Hjort and Claes kens in 2003.One inconvenience,however,of the existing works is that they usually require the involved criterion function to be twice differentiable which thus prevents a direct application to the case of quantile regression(QR).This as well as some other intrinsic merits of QR motivate us to study the FIC and FMA in a locally misspecified linear QR model.Specifically,we derive in this paper the explicit asymptotic risk expression for a general submodel-based QR estimator of a focus parameter.Then based on this asymptotic result,we develop the FIC and FMA in the current setting.Our theoretical development depends crucially on the convexity of the objective function,which makes possible to establish the asymptotics based on the existing convex stochastic process theory.Simulation studies are presented to illustrate the finite sample performance of the proposed method.The low birth weight data set is analyzed.
基金Supported in part by the National Natural Science Foundation of China under Grant No.11171006
文摘The main purpose of this paper is using capture-recapture data to estimate the population size when some covariate values are missing, possibly non-ignorable. Conditional likelihood method is adopted, with a sub-model describing various missing mechanisms. The derived estimate is proved to be asymptotically normal, and simulation studies via a version of EM algorithm show that it is approximately unbiased. The proposed method is applied to a real example, and the result is compared with previous ones.
