STATISTICAL INFERENCE FOR SPARSE CATEGORICAL DATA

稀疏分类数据的统计推断

• 批准号: 2910076
• 负责人: Alan Agresti
• 金额: $ 8.35万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 1990
• 资助国家: 美国
• 起止时间: 1990-05-01 至 2001-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2910076
• 关键词: computer data analysis; data collection methodology /evaluation; health science research analysis /evaluation; mathematical model; method development; model design /development; statistics /biometry

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.

描述：健康相关科学中的变量通常是分类的。 显示这些数据的表格通常是稀疏的， 来分类，因为（1）研究可能有少量的受试者， 或（2）重复测量响应可能产生许多细胞， 表. 拟议的研究重点是统计方法的发展 稀疏分类数据。 小样本方法用于比较 当样本量不足以进行大样本时的医疗 近似值 重复测量数据的模型可用于 允许受试者异质性，例如在比较 交叉研究，评估评估者之间关于医疗 条件，模拟眼科研究中的配对反应，以及 在公共卫生应用中评估人口规模， 捕获-再捕获模型 重复分类测量数据：将开发方法， 描述聚类分类数据。 本研究的一个共同主题将 是含随机效应的广义线性混合模型的应用。 具体的主题包括研究在何种程度上可以使用 回归模型中随机效应的无分布方法 多变量分类响应，基于 多变量二项logit正态分布，描述 顺序优势比，并使用混合模型的应用，如 捕获-再捕获估计 小样本分析：用于推断 将进一步开发分类数据模型中的参数， 评估。 要考虑的主题包括近似置信区间 对于二项式和泊松参数以及比较这些参数的测量， 线性logit中趋势参数的改进精确置信区间 模型，小样本推理的顺序限制方法，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值的处理。