Variable (feature) selection has attracted much attention in contemporary statistical learning and recent scientific research. This is mainly due to the rapid advancement in modern technology that allows scientists to collect data of unprecedented size and complexity. One type of statistical problem in such applications is concerned with modeling an output variable as a function of a sma...

Teaching of machine learning should aim at the readiness to understand and implement modern machine learning algorithms. Towards this goal, we often have course exercises involving the student to solve a practical machine learning problem involving a reallife data set. The students implement the programs of machine learning methods themselves and gain deep insight on the implementation details ...

Finite mixture models, which are a type of latent variable model, express the overall distribution of one or more variables as a mixture of a finite number of component distributions. In direct applications, one assumes that the overall population heterogeneity with respect to a set of manifest variables results from the existence of two or more distinct homogeneous subgroups, or latent classes...

Modeling consumer heterogeneity helps practitioners understand market structures and devise effective marketing strategies. In this research the authors study finite mixture specifications for modeling consumer heterogeneity when each regression coefficient has its own finite mixture, that is, an attribute finite mixture model. An important challenge of such an approach to modeling heterogeneit...