We considered the following semiparametric regres-sion model yi = X iT β+ s ( t i ) + ei (i =1,2,,n). First,the general-ized ridge estimators of both parameters and non-parameters are given without a restrained desig...We considered the following semiparametric regres-sion model yi = X iT β+ s ( t i ) + ei (i =1,2,,n). First,the general-ized ridge estimators of both parameters and non-parameters are given without a restrained design matrix. Second,the generalized ridge estimator will be compared with the penalized least squares estimator under a mean squares error,and some conditions in which the former excels the latter are given. Finally,the validity and feasibility of the method is illustrated by a simulation example.展开更多
基金Supported by the Key Project of Chinese Ministry of Educa-tion (209078)the Scientific Research Item of Hubei Provincial Department of Education (D20092207)
文摘We considered the following semiparametric regres-sion model yi = X iT β+ s ( t i ) + ei (i =1,2,,n). First,the general-ized ridge estimators of both parameters and non-parameters are given without a restrained design matrix. Second,the generalized ridge estimator will be compared with the penalized least squares estimator under a mean squares error,and some conditions in which the former excels the latter are given. Finally,the validity and feasibility of the method is illustrated by a simulation example.
