We introduce a tree-based approach for assessing the performance impact of diverse self-selected interventions in management research. Our approach, which takes advantage of "Big Data", or observational data with large sample sizes and a large number of variables, offers important advantages...

The main goal of biosurveillance is the early detection of disease outbreaks. Advances in technology have allowed the collection, transfer, and storage of pre-diagnostic information in addition to traditional diagnostic data. Such data carry the potential of an earlier outbreak signature. In...

Overview of the data mining process -- Data visualization -- Dimension reduction -- Evaluating predictive performance -- Multiple linear regression -- k-Nearest Neighbors (kNN) -- The Naive Bayes classifier -- Classification and regression trees -- Logistic regression -- Neural nets --...

Prediction and variable selection are major uses of data mining algorithms but they are rarely the focus in social science research, where the main objective is causal explanation. Ideal causal modeling is based on randomized experiments, but because experiments are often impossible, unethical...

The current kidney allocation system in the United States fails to match donors and recipients well. In an effort to improve the allocation system, the United Network of Organ Sharing (UNOS) defined factors that should determine a new allocation policy, and particularly patients' potential...

Generating multivariate Poisson random variables is essential in many applications, such as multi echelon supply chain systems, multi-item / multi-period pricing models, accident monitoring systems, etc. Current simulation methods suffer from limitations ranging from computational complexity to...