STATISTICAL METHODS FOR A MIXTURE OF SUBPOPULATIONS

本研究将探讨估计的内在困难 当观测值的身份是 不完全知道。 硬聚类和模糊聚类技术， 将观测值分成子组进行研究。 本研究将 为亚群特征制定估算方法。 将研究新方法的统计特性。 的 将新方法与现有方法进行比较 旨在纠正错误分类问题。 新方法 将减轻异构系统的一些内在困难， 亚群的混合物。 将申请研究这些问题 关于出生时的生物异质性和测量误差， 体重、胎龄和围产期死亡率。 这项研究还将 应用于宫内发育和妊娠的研究 结果。 混合发行版的另一个主题是开发和 比较参数和非参数检验以评估同质性 临床试验和其他生物医学试验的混合模型中的假设 问题研究 将研究混合参数的估计。 的 Yu（1991，comm Stat）开发的方法将扩展到混合 从两个亚群的混合物的K亚群的分布。 将研究数值算法。

This study will examine the intrinsic difficulty of estimating subpopulation characteristics when the identities of the observations are not completely known. Hard clustering and fuzzy clustering techniques to classify observations into subgroups are investigated. This study will develop estimation methodologies for the subpopulation characteristics. The statistical properties of the new methodologies will be studied. The new methodologies will be compared with the existing methodologies designed to remedy the misclassification problem. The new methodologies will alleviate some of the intrinsic difficulties for a heterogeneous mixture of subpopulations. Applications will be made to study the issues concerning biological heterogeneity and measurement error in the birth weight, gestational age and perinatal mortality. This study will also be applied to the investigation of intrauterine growth and pregnancy outcome. Another topic in mixture of distributions is to develop and compare parametric and nonparametric tests to assess homogeneity assumption in mixed models for clinical trials and other biomedical studies. Estimation of mixing parameters will be investigated. The methods developed in Yu (1991, comm Stat) will be extended to a mixing distribution of K subpopulation from a mixture of two subpopulations. Numerical algorithms will be studied.