Random forest is a classification algorithm well suited for microarray data: it shows excellent performance even when most predictive variables are noise, can be used when the number of variables is much larger than the number of observations, and returns measures of variable importance. Thus, it is important to understand the performance of random forest with microarray data and its use for ge...

This paper is concerned with developing rules for assignment of tooth prognosis based on actual tooth loss in the VA Dental Longitudinal Study. It is also of interest to rank the relative importance of various clinical factors for tooth loss. A multivariate survival tree procedure is proposed. The procedure is built on a parametric exponential frailty model, which leads to greater computational...

The Award Committee – Steffen Dereich, TU Berlin, Germany, and Frances Kuo, University of New South Wales, Australia — determined that the following two papers exhibited exceptional merit and therefore awarded the prize to: AickeHinrichs, for paper ‘‘Optimal importance sampling for the approximation of integrals’’, which appeared in April, 2010, vol. 26, pp. 125–134. Simon Foucart, Alain Pajor,...

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We use panel probit models with unobserved heterogeneity and serially correlated errors in order to analyze the determinants and the dynamics of current-account reversals for a panel of developing and emerging countries. The likelihood evaluation of these models requires high-dimensional integration for which we use a generic procedure known as Efficient Importance Sampling (EIS). Our empirical...

Despite growing interest and practical use in various scientific areas, variable importances derived from tree-based ensemble methods are not well understood from a theoretical point of view. In this work we characterize the Mean Decrease Impurity (MDI) variable importances as measured by an ensemble of totally randomized trees in asymptotic sample and ensemble size conditions. We derive a thre...