Random forest is in many fields of research a common method for data driven predictions. Within economics and prediction of poverty, random forest is rarely used. Comparing out-of-sample predictions in surveys for same year in six countries shows that random forest is often more accurate than...

This is the third in a series of papers aimed at providing models effective in predicting the degree of pain and discomfort in canines. The first two papers provided benchmarking and examination of dogs suffering from osteoarthritis (OA). In this chapter, we extend the study to include dogs...

This chapter addresses the general process of determining the value of a particular company, with additional detail on how valuation processes might be adapted to produce credible value conclusions of emerging technology ventures. There are three primary approaches to business valuation. There...

Atheoretical regression trees (ART) are applied to detect changes in the mean of a stationary long memory time series when location and number are unknown. It is shown that the BIC, which is almost always used as a pruning method, does not operate well in the long memory framework. A new method...

In this paper we propose a smooth transition tree model for both the conditional mean and variance of the short-term interest rate process. The estimation of such models is addressed and the asymptotic properties of the quasi-maximum likelihood estimator are derived. Model specification is also...

The aim of this paper is twofold. First we discuss the origin of tree methods. Essentially we survey earlier methods that led to CHAID (Kass, 1980; Biggs et al., 1991). The second goal is then to explain in details the functioning of CHAID, especially the differences between the original method as...