In this paper, we consider the general growth curve model with multivariate random effects covariance structure and provide a new simple estimator for the parameters of interest. This estimator is not only convenient for testing the hypothesis on the corresponding parameters, but also has higher efficiency than the least-square estimator and the improved two-stage estimator obtained by Rao under certain conditions. Moreover, we obtain the necessary and sufficient condition for the new estimator to be identical to the best linear unbiased estimator. Examples of its application are given.
New biomarkers continue to be developed for the purpose of diagnosis, and their diagnostic performances are typically compared with an existing reference biomarker used for the same purpose. Considerable amounts of research have focused on receiver operating characteristic curves analysis when the reference biomarker is dichotomous. In the situation where the reference biomarker is measured on a continuous scale and dichotomization is not practically appealing, an index was proposed in the literature to measure the accuracy of a continuous biomarker, which is essentially a linear function of the popular Kendall’s tau. We consider the issue of estimating such an accuracy index when the continuous reference biomarker is measured with errors. We first investigate the impact of measurement errors on the accuracy index, and then propose methods to correct for the bias due to measurement errors. Simulation results show the effectiveness of the proposed estimator in reducing biases. The methods are exemplified with hemoglobin A1c measurements obtained from both the central lab and a local lab to evaluate the accuracy of the mean data obtained from the metered blood glucose monitoring against the centrally measured hemoglobin A1c from a behavioral intervention study for families of youth with type 1 diabetes.
In this paper, we are concerned with the optimal boundary control of a continuum model for a highly re-entrant manufacturing system. Using the Dubovitskii–Milyutin functional analytical approach, we establish the Pontryagin maximum principles for the optimal boundary control problems in both fixed and free final time horizon cases. A remark is then made about the application of the obtained results.
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