Bayesian Additive Regression Trees (BART) is a tree-based machine learning method that has been successfully applied to regression and classification problems. BART assumes regularisation priors on set of trees work as weak learners very flexible for predicting in the presence non-linearity high-order interactions. In this paper, we introduce an extension BART, called Model (MOTR-BART), conside...

This article describes how to use statistical data analysis to obtain models directly from data. The focus is put on finding nonlinearities within a generalized additive model. These models are found by means of backfitting or more general algorithms, like the alternating conditional expectation value one. The method is illustrated by numerically generated data. As an application, the example o...

We present a growing dimension asymptotic formalism. The perspective in this paper is classification theory and we show that it can accommodate probabilistic networks classi­ fiers, including naive Bayes model and its augmented version. When represented as a Bayesian network these classifiers have an im­ portant advantage: The corresponding dis­ criminant function turns out to be a spe­ cialize...

The relationship between blue whale (Balaenoptera musculus) visual and acoustic encounter rates was quantitatively evaluated using hourly counts of detected whales during shipboard surveys off southern California. Encounter rates were estimated using temporal, geographic, and weather variables within a generalized additive model framework. Visual encounters (2.06 animals/h, CV = 0.10) varied wi...

In our daily lives we often make quantitative judgments based on multiple pieces of information such as evaluating a student’s paper based on form and content. Psychological research suggests that humans rely on several strategies to make multiple-cue judgments. The strategy that is used depends on the structure of the task. In contrast, recent research on learning in judgment tasks suggests th...

The multivariate regression function estimation is an important problem which has been extensively treated for discrete time processes. It is well-known from (11) that the additive regression models bring out a solution to the problem of the curse of dimensionality in nonparametric multivariate regression estimation, which is characterized by a loss in the rate of convergence of the regression ...

Previous studies have demonstrated that river-based surveys can provide an inexpensive source of information for neotropical zoologists, yet little information is available to inform the application of this technique for the long term monitoring of neotropical turtle species. We aimed to fill this gap by presenting an assessment of data collected during 333 river surveys over 50 months along ri...

Competing risk failure time data occur frequently in medical studies, and a number of methods have been proposed for the analysis of these data. To assess covariate effects, a standard approach is to model the cause-specific hazard functions of different failure types. Recently, Fine and Gray (1999) proposed directly modeling the subdistribution of a competing risk with a Cox type model. In thi...