A partially linear regression model with heteroscedastic and/or serially correlated errors is studied here. It is well known that in order to apply the semiparametric least squares estimation （SLSE） to make statisti...A partially linear regression model with heteroscedastic and/or serially correlated errors is studied here. It is well known that in order to apply the semiparametric least squares estimation （SLSE） to make statistical inference a consistent estimator of the asymptotic covariance matrix is needed. The traditional residual-based estimator of the asymptotic covariance matrix is not consistent when the errors are heteroscedastic and/or serially correlated. In this paper we propose a new estimator by truncating, which is an extension of the procedure in White. This estimator is shown to be consistent when the truncating parameter converges to infinity with some rate.展开更多
In many settings,multiple data collections and analyses on the same topic are summarised separately through statistical estimators of parameters and variances,and yet there are scientificreasons for sharing some stati...In many settings,multiple data collections and analyses on the same topic are summarised separately through statistical estimators of parameters and variances,and yet there are scientificreasons for sharing some statistical parameters across these different studies.This paper summarises what is known from large-sample theory about when estimators of a common structuralparameter from several independent samples can be combined functionally,or more specificallylinearly,to obtain an asymptotically efficient estimator from the combined sample.The main ideais that such combination can be done when the separate-sample nuisance parameters,if anyexist,vary freely and independently of one another.The issues are illustrated using data from amulti-centre lung cancer clinical trial.Examples are presented to show that separate estimatorscannot always be combined in this way,and that the functionally combined separate estimators may have low or 0 efficiency compared to the unified analysis that could be performed bypooling the datasets.展开更多
基金Zhou's research was partially supported by the National Natural Science Foundation of China(No.10471140,10571169)
文摘A partially linear regression model with heteroscedastic and/or serially correlated errors is studied here. It is well known that in order to apply the semiparametric least squares estimation （SLSE） to make statistical inference a consistent estimator of the asymptotic covariance matrix is needed. The traditional residual-based estimator of the asymptotic covariance matrix is not consistent when the errors are heteroscedastic and/or serially correlated. In this paper we propose a new estimator by truncating, which is an extension of the procedure in White. This estimator is shown to be consistent when the truncating parameter converges to infinity with some rate.
基金The authors gratefully acknowledge the Eastern Cooperative Oncology Group as the source for the ECOG EST 1582dataset,and the suggestion of a referee to expand our treatment of(V)to estimating equations.
文摘In many settings,multiple data collections and analyses on the same topic are summarised separately through statistical estimators of parameters and variances,and yet there are scientificreasons for sharing some statistical parameters across these different studies.This paper summarises what is known from large-sample theory about when estimators of a common structuralparameter from several independent samples can be combined functionally,or more specificallylinearly,to obtain an asymptotically efficient estimator from the combined sample.The main ideais that such combination can be done when the separate-sample nuisance parameters,if anyexist,vary freely and independently of one another.The issues are illustrated using data from amulti-centre lung cancer clinical trial.Examples are presented to show that separate estimatorscannot always be combined in this way,and that the functionally combined separate estimators may have low or 0 efficiency compared to the unified analysis that could be performed bypooling the datasets.
