Workshop on Categorical Data Analysis

分类数据在许多应用中被收集。随着计算资源和计算能力的广泛应用，一套全面的分类数据方法开始出现。 然而，重要的开放研究问题仍然存在。随着越来越大的分类数据集被收集，分析高维数据的挑战变得更加普遍。对于分类数据，评估相关性结构并对其进行适当建模比正态分布数据更有问题。类似地，当缺失数据不可估计时，广义估计方程通常会导致有偏估计量。分类数据的混合模型和平滑技术是重要的研究领域。贝叶斯方法已成为流行的模型平均和模型选择程序。现在特别感兴趣的一个领域是贝叶斯诊断的发展（例如，残差和后验预测概率），其是拟合模型的副产品。在这次研讨会上，分类方法学领域的研究领导者和年轻研究人员将聚集在一起。十一位领导人将在研究前沿发表演讲。年轻的研究人员将有机会展示展示他们的研究海报，并与经验丰富的研究人员进行广泛的访问。许多应用程序会产生分类数据，例如性别，种族和教育水平。许多传统的统计方法假设数据来自正态分布，并且不适合分类数据。随着计算资源的可用性和能力的提高，已经开发了一套全面的分类统计方法。然而，还需要更多的统计方法。在遗传学中，从每个个体收集大量的分类反应，导致复杂的分析问题。在许多研究中，一些数据缺失。这可能是因为一个人退出了研究，拒绝回答，等等。有时缺失的数据随机发生，缺失可能会被忽略。在其他时候，缺失的数据并不是随机的，比如当一种治疗让人们感觉不好，他们就会退出。这种缺失不容忽视。这只是研讨会期间要探讨的两个研究领域。研讨会将提供一个极好的机会，讨论分类方法学的许多最近的重大发展，并确定重要的问题和新的研究方向。 本次研讨会将确定在新技术驱动的应用领域中似乎最有效的方法。

Categorical data are collected in numerous applications. As computing resources and power have become widely available, a comprehensive set of categorical data methods has begun to emerge. Yet, important open research questions remain. As increasingly large categorical data sets are collected, challenges of analyzing high dimensional data become more common. Assessing the correlation structure and properly modeling it are more problematic for categorical data than for normally distributed data. Similarly, when missing data are non-ignorable, generalized estimating equations usually result in biased estimators. Mixed models and smoothing techniques for categorical data are important areas of research. Bayesian methods have become popular for model averaging and model selection procedures. An area of particular interest now is the development of Bayesian diagnostics (e.g., residuals and posterior predictive probabilities) that are a by-product of fitting a model. In this workshop, research leaders and young researchers in the field of categorical methodology will be brought together. Eleven leaders will present lectures at the cutting-edge of research. Young researchers will have the opportunity to present posters show-casing their research and to visit extensively with seasoned researchers.Numerous applications give rise to categorical data, e.g. sex, race, and education level. Many of the traditional statistical methods assume that the data are from a normal distribution and are not appropriate for categorical data. With the increased availability and power of computational resources, a comprehensive set of statistical methods for categorical has been developed. However, additional statistical methods are needed. In genetics, large numbers of categorical responses are collected from each individual, leading to complex analytical questions. In many studies, some data are missing. This could arise because a person drops out of a study, refuses to respond, etc. Sometimes the missing data occur randomly, and the missingness may be ignored. At other times, the missing data is not at random, such as when a treatment makes people feel badly and they drop out. Such missingness cannot be ignored. These are but two research areas to be explored during the workshop. The workshop will provide an excellent opportunity to discuss the many recent significant developments in categorical methodology and to identify important problems and new research directions. This workshop will identify the methods that seem to work best in areas of application driven by new technology.