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.
