Title: Nonparametric predictive inference for 2x2 contingency tables
Authors: Reid Alotaibi - Durham University (United Kingdom) [presenting]
Abstract: Nonparametric predictive inference (NPI) is a frequentist statistics approach that makes few assumptions, enabled by using lower and upper probabilities to quantify uncertainty, and explicitly focuses on future observations. NPI has been developed for a range of data types, and for a variety of applications and problems in statistics. In statistics, data in the form of so-called contingency tables occur in many applications. Such tables show the distribution of one variable in rows and another variable in columns, and are typically used to study the dependence between the two variables (with generalization to more than two variables). The most basic version is the 2x2 table, where both variables are binary. In addition to studying the dependence, one may be interested in further inference based on such data. NPI method for such data has been developed where the inferences are restricted to only one future observation; however, considering more general inferences about multiple future observations for a 2x2 contingency which will be briefly presented.