MISSING/MISMEASURED VARIABLES--METHODS AND APPLICATIONS

描述：该建议解决了以下建模和估计问题 由于缺少数据和/或测量错误的变量而出现。该项目包括 三个主要领域：频繁性多重归罪程序 研究将考虑参数、非参数和半参数 接近了。要解决的问题包括提出新的方法 推论，推导得到的估计量的性质，提出一个 方法比较不同程序之间的效率和开发 推理程序。目标是(1)提供最高效的 参数和半参数模型的多重补偿过程和 (2)为效率较低但最容易的人开发推理程序 实施的估计器。 混合效应药代动力学模型中的测量误差问题 这一研究领域的目标包括：(1)确定 经典测量误差模型的测量误差和 Berkson类型的测量误差模型，(2)开发图形化工具 其可用于检测测量误差影响的严重性 以及(3)提供调整两者的测量误差的方法 经典药物动力学模型和临床药物动力学模型。这项研究将 包含理论渐近分析，并辅以广泛的 模拟和真实数据应用。 数据驱动平滑参数确定中的重采样方法 研究将把Wang(1996)的Bootstrap方法推广到 半参数异方差回归模型到半参数 分析含有缺失/错误测量变量的数据的程序。因为 在技术困难中，大多数的平滑参数 半参数程序通常由特别方法确定。这个 所提出的方法将易于实施和估计平滑 参数以自动方式显示。这项研究的成功将有助于 推广半参数程序的使用。

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.