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Logistic reduced rank regression is a useful data analysis tool when we have multiple binary response variables and a set of predictors. In this paper, we describe logistic reduced rank regression and present a new majorization minimization algorithm for the estimation of model parameters....
Relational event network data are becoming increasingly available. Consequently, statistical models for such data have also surfaced. These models mainly focus on the analysis of single networks; while in many applications, multiple independent event sequences are observed, which are likely to...
Random forests are currently one of the most popular algorithms for supervised machine learning tasks. By taking into account for many trees instead of a single one the resulting forest model is no longer easy to understand and also often denoted as a black box. The paper is dedicated to the...
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