This course introduces principles and analyses related to data with categorical outcomes. This course will consider topics such as probability distributions with categorical data, contingency table analysis, the generalized linear model, logit models and loglinear models. Students are expected to: a) learn to select methods appropriate for a question of interest for data with a categorical outc...

while ignoring income distribution and estimating aggregate demand generally leads to aggregation bias and thus, does not reflect households’ demand properly, yet aggregation bias is rarely considered in empirical demand studies and simply is ignored in most cases. the objective of the present study is to estimate aggregation bias in households’ demand for various types of meats and to evaluate...

Ling Guo. Randomization Based Privacy Preserving Categorical Data Analysis. Under the direction of Dr. Xintao Wu The success of data mining relies on the availability of high quality data. To ensure quality data mining, effective information sharing between organizations becomes a vital requirement in today’s society. Since data mining often involves sensitive information of individuals, the pu...

The data stream model has been defined for new classes of applications involving massive data being generated at a fast pace. Web click stream analysis and detection of network intrusions are two examples. Cluster analysis on data streams becomes more difficult, because the data objects in a data stream must be accessed in order and can be read only once or few times with limited resources. Rec...

Graphical methods for quantitative data are well-developed, and widely used in both data analysis (e.g., detecting outliers, verifying model assumptions) and data presentation. Graphical methods for categorical data, however, are only now being developed, and are not widely used. This paper outlines a general framework for data visualization methods in terms of communication goal (analysis vs. ...

A key issue when mining web information is the labeling problem: data is abundant on the web but is unlabelled. In this thesis, we address this problem by proposing i) a novel theoretical granular model that structures categorical noun phrase instances as well as semantically related noun phrase pairs from a given corpus representing unstructured web pages with a variant of Tolerance Rough Sets...

Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a mod...

abstract the purpose of this paper is to test the hypothesis first proposed by romer (1993); suggesting that inflation is lower in more open economies. according to this hypothesis, central banks have a lower incentive to engineer surprise inflations in more-open economies because the phillips curve is steeper. furthermore, comparing with other empirical studies, this paper has used the new kof...

Recent studies in classification have proposed ways of exploiting the association rule mining paradigm. These studies have performed extensive experiments to show their techniques to be both efficient and accurate. However, existing studies in this paradigm either do not provide any theoretical justification behind their approaches or assume independence between some parameters. In this thesis,...