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...

Poisson regression is a popular tool for modeling count data and is applied in a vast array of applications from the social to the physical sciences and beyond. Real data, however, are often over- or under-dispersed and, thus, not conducive to Poisson regression. We propose a regression model...

Censored count data are encountered in many applications, often due to a data collection mechanism that introduces censoring. A common example is questionnaires with question answers of the type 0,1,2,3. We consider the problem of predicting a censored output variable Y, given a set of complete...

Functional data analysis can be challenging when the functional objects are sampled only very sparsely and unevenly. Most approaches rely on smoothing to recover the underlying functional object from the data which can be difficult if the data is irregularly distributed. In this paper we present...