Applied Statistics Commons^™

The Fraud Detection Triangle: A New Framework For Selecting Variables In Fraud Detection Research, Adrian Gepp, Kuldeep Kumar, Sukanto Bhattacharya

Adrian Gepp

Marginal Structural Models: An Application To Incarceration And Marriage During Young Adulthood, Valerio Bacak, Edward Kennedy

Edward H. Kennedy

Advanced methods for panel data analysis are commonly used in research on family life and relationships, but the fundamental issue of simultaneous time-dependent confounding and mediation has received little attention. In this article the authors introduce inverse-probability-weighted estimation of marginal structural models, an approach to causal analysis that (unlike conventional regression modeling) appropriately adjusts for confounding variables on the causal pathway linking the treatment with the outcome. They discuss the need for marginal structural models in social science research and describe their estimation in detail. Substantively, the authors contribute to the ongoing debate on the effects of incarceration on marriage …

Predicting Financial Distress: A Comparison Of Survival Analysis And Decision Tree Techniques, Adrian Gepp, Kuldeep Kumar

Adrian Gepp

Financial distress and then the consequent failure of a business is usually an extremely costly and disruptive event. Statistical financial distress prediction models attempt to predict whether a business will experience financial distress in the future. Discriminant analysis and logistic regression have been the most popular approaches, but there is also a large number of alternative cutting – edge data mining techniques that can be used. In this paper, a semi-parametric Cox survival analysis model and non-parametric CART decision trees have been applied to financial distress prediction and compared with each other as well as the most popular approaches. This …