MISSING/MISMEASURED VARIABLES--METHODS AND APPLICATIONS

描述：该提案解决了建模和估计问题， 数据缺失和/或变量测量错误。 该项目由 三个主要领域：频率主义多重插补程序}： 研究将考虑参数、非参数和半参数 接近。 要解决的问题包括提出新的方法， 插补，推导所得估计量的性质，提出一个 方法比较各工序的效率， 推理程序 目标是（1）提供最有效的 参数和半参数模型的多重插补程序， (2)为效率较低但最容易实现的 实现的估计器。 混合效应药代动力学模型中的测量误差问题： 该研究领域的目标包括（1）确定 经典测量误差模型和 Berkson型测量误差模型;（2）图形化工具的开发 其可用于检测测量误差影响的严重性 以及（3）提供调整两种测量误差的方法， 经典和临床药代动力学模型。 这项研究将 包含理论上的渐近分析， 模拟和真实数据应用。 数据驱动平滑参数确定中的恢复方法： 研究将推广Wang（1996）的bootstrap方法， 半参数异方差回归模型 分析缺失/错误测量变量的数据的程序。 因为 的技术难点，平滑参数的大部分， 半参数过程通常通过特别方法确定。 的 提出的方法将易于实现和估计的平滑 参数自动化。 这项研究的成功将有助于 促进半参数程序的使用。

DESCRIPTION: This proposal addresses modeling and estimation problems that arise with missing data and/or mismeasured variables. The project consists of three major areas: Frequentist Multiple Imputation Procedures}: The research will consider parametric, nonparametric and semiparametric approaches. The problem to be addressed include proposing new methods of imputation, deriving the properties of the resulting estimators, proposing a method to compare the efficiencies among the procedures and developing inference procedures. The goals are (1) providing the most efficient multiple imputation procedure for parametric and semiparametric models and (2) developing inference procedures for the less efficient but most easily implemented estimators. Measurement error problems in mixed effects pharmacokinetics models: The goals for this research area include (1) determining the effect of measurement errors for both classical measurement error models and the Berkson type of measurement error models, (2) developing graphical tools which can be used to detect the severity of the measurement error effects and (3) providing methods which adjust for the measurement errors for both the classical and the clinical pharmacokinetics models. The study will contain theoretical asymptotic analysis supplemented by extensive simulations and real-data applications. Resampling methods in data-driven smoothing parameter determination: This research will generalize the bootstrap method of Wang (1996) for semi-parametric heteroscedastic regression models to semiparametric procedures which analyze data with missing/mismeasured variables. Because of the technical difficulties, the smoothing parameters for most of the semiparametric procedures have often been determined by ad hoc methods. The approaches proposed will be easy to implement and estimate the smoothing parameters in an automatic fashion. The success of this research will help to promote the use of semiparametric procedures.