This paper employs the SCAD-penalized least squares method to simultaneously select variables and estimate the coefficients for high-dimensional covariate adjusted linear regression models.The distorted variables are ...This paper employs the SCAD-penalized least squares method to simultaneously select variables and estimate the coefficients for high-dimensional covariate adjusted linear regression models.The distorted variables are assumed to be contaminated with a multiplicative factor that is determined by the value of an unknown function of an observable covariate.The authors show that under some appropriate conditions,the SCAD-penalized least squares estimator has the so called "oracle property".In addition,the authors also suggest a BIC criterion to select the tuning parameter,and show that BIC criterion is able to identify the true model consistently for the covariate adjusted linear regression models.Simulation studies and a real data are used to illustrate the efficiency of the proposed estimation algorithm.展开更多
Recurrent events data with a terminal event （e.g., death） often arise in clinical and ob- servational studies. Variable selection is an important issue in all regression analysis. In this paper, the authors first pr...Recurrent events data with a terminal event （e.g., death） often arise in clinical and ob- servational studies. Variable selection is an important issue in all regression analysis. In this paper, the authors first propose the estimation methods to select the significant variables, and then prove the asymptotic behavior of the proposed estimator. Furthermore, the authors discuss the computing algorithm to assess the proposed estimator via the linear function approximation and generalized cross validation method for determination of the tuning parameters. Finally, the finite sample estimation for the asymptotical covariance matrix is also proposed.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.11471029,11101014,61273221 and 11171010the Beijing Natural Science Foundation under Grant Nos.1142002 and 1112001+1 种基金the Science and Technology Project of Beijing Municipal Education Commission under Grant No.KM201410005010the Research Fund for the Doctoral Program of Beijing University of Technology under Grant No.006000543114550
文摘This paper employs the SCAD-penalized least squares method to simultaneously select variables and estimate the coefficients for high-dimensional covariate adjusted linear regression models.The distorted variables are assumed to be contaminated with a multiplicative factor that is determined by the value of an unknown function of an observable covariate.The authors show that under some appropriate conditions,the SCAD-penalized least squares estimator has the so called "oracle property".In addition,the authors also suggest a BIC criterion to select the tuning parameter,and show that BIC criterion is able to identify the true model consistently for the covariate adjusted linear regression models.Simulation studies and a real data are used to illustrate the efficiency of the proposed estimation algorithm.
文摘Recurrent events data with a terminal event （e.g., death） often arise in clinical and ob- servational studies. Variable selection is an important issue in all regression analysis. In this paper, the authors first propose the estimation methods to select the significant variables, and then prove the asymptotic behavior of the proposed estimator. Furthermore, the authors discuss the computing algorithm to assess the proposed estimator via the linear function approximation and generalized cross validation method for determination of the tuning parameters. Finally, the finite sample estimation for the asymptotical covariance matrix is also proposed.
