STATISTICAL INFERENCE FOR SPARSE CATEGORICAL DATA

描述：在与健康相关的科学中，变量通常是绝对的。 显示此类数据的表通常很稀疏，在 分类，因为(1)研究可能只有少量的受试者， 或(2)重复测量反应可能会产生许多细胞用于 桌子。拟议的研究侧重于统计方法的发展 用于稀疏分类数据。小样本方法对于比较很有用 大样本量不足时的医疗处理 近似值。重复测量数据的模型对于 允许受试者的异质性，例如在比较 交叉研究，评估评分者之间关于医疗的一致性 条件，对眼科研究中的配对反应进行建模，以及 评估公共卫生应用中的人口规模 捕获-再捕获模型。 重复分类测量数据：将开发方法来 描述聚集的分类数据。这项研究的一个共同主题是 BE是包含随机效应的广义线性混合模型的应用。 具体主题包括研究一个人可以在多大程度上使用 回归模型中随机效应的无分布方法 多变量分类反应，开发了一个基于 多元二项-Logit正态分布，描述了 序数赔率，并将混合模型用于以下应用 捕获-重新捕获估计。 小样本分析：推断的小样本方法 分类数据模型中的参数将进一步开发和 已评估。要考虑的主题包括近似置信度区间 对于二项式和泊松参数以及比较这些参数的测量， 改进的线性Logit中趋势参数的精确可信区间 模型，用于小样本推理的阶数受限方法，仅假设 参数的单调排序，而不是完全参数模型，以及 多中心临床试验中抽样零的处理。

DESCRIPTION: Variables in health-related sciences are often categorical. Tables displaying such data are often sparse, having few observations in come categories, because (1) the study may have a small number of subjects, or (2) repeated measurement of responses may produce many cells for the table. The proposed research focuses on development of statistical methods for sparse categorical data. Small-sample methods are useful for comparing medical treatments when the sample size is insufficient for large-sample approximations. Models for repeated measurement data are useful for allowing subject heterogeneity, for instance in comparing treatments in cross-over studies, assessing inter-rater agreement about a medical condition, modeling matched-pairs responses in opthalmalogic research, and assessing population size in public health applications with capture-recapture models. Repeated categorical measurement data: Methods will be developed to describe clustered categorical data. A common theme of this research will be application of generalized linear mixed models containing random effects. Specific topics include studying the extent to which one can use a distribution-free approach for the random effects in regression models for multivariate categorical responses, developing a parametric model based on a multivariate binomial-logit normal distribution, describing heterogeneity of ordinal odds ratios, and using mixed models for applications such as capture-recapture estimation. Small-sample analyses: Small-sample methods for making inferences about parameters in models for categorical data will be further developed and evaluated. Topics to be considered include approximate confidence intervals for binomial and Poisson parameters and measures comparing such parameters, improved exact confidence intervals for the trend parameters in linear logit models, order-restricted methods for small-sample inference that assume only monotone orderings on parameters rather than a fully parametric model, and treatment of sampling zeroes in multi-center clinical trails.