Latent class models offer an alternative perspective to the popular mixed logit form, replacing the continuous distribution with a discrete distribution in which preference heterogeneity is captured by membership of distinct classes of utility description. Within each class, preference homogeneity is usually assumed (i.e., fixed parameters), although interactions with observed contextual effect...

We consider here the problem of classifying a macro-level object based on measurements of embedded (micro-level) observations within each object, for example, classifying a patient based on measurements on a collection of a random number of their cells. Classification problems with this hierarchical, nested structure have not received the same statistical understanding as the general classifica...

Clustering of variables is the task grouping similar into different groups. It may be useful in several situations such as dimensionality reduction, feature selection, and detect redundancies. In present study, we combine two methods features clustering around latent (CLV) algorithm k-means based co-clustering (kCC). Indeed, classical CLV cannot applied to high dimensional data because this app...

The unique goal of the Living Computer Museum (LCM) in Seattle, Washington, is to keep old computer technology powered up and ready to be used by patrons. Instead of sheltering its rare arti facts in climate-controlled glass cases, most of the computers at the LCM are turned on and accompanied by a user manual to read, a chair to sit in, and a keyboard to type on so you can explore what it was ...