We introduce Bayesian additive regression trees (BART) for log-linear models including multinomial logistic and count with zero-inflation overdispersion. BART has been applied to nonparametric mean binary classification problems in a range of settings. However, existing applications have mostly limited Gaussian “data,” either observed or latent. This is primarily because efficient MCMC algorith...

High dividend yields imply that either future dividend growth must be low, or future discount rates must be high, or both. While previous studies have focused on the predictability of expected returns from dividend yields, dividend yields also strongly predict future dividends, and the predictability of dividend growth is much stronger than discount rates returns at a 1-year horizon. Inference ...

In this work, we develop and evaluate a wide range of feature spaces for deriving Levinstyle verb classifications (Levin, 1993). We perform the classification experiments using Bayesian Multinomial Regression (an efficient log-linear modeling framework which we found to outperform SVMs for this task) with the proposed feature spaces. Our experiments suggest that subcategorization frames are not...

This paper gives the results of a semiparametric analysis of pollution effects on housing prices using the Boston Housing Data. The exposition introduces the basic ideas of modeling pollution impacts with hedonic price methods, discusses the standard log-linear model, and then introduces nonparametric estimation and semiparametric index models. We focus on the intuitive content and substantive ...

The assessment of the influence of individual observations on the outcome of the analysis by perturbation has received a lot of attention for situations in which the observations are independent and identically distributed. However, no methods based on minor perturbations for carrying out such assessments are available in the context of multinomial models. A simultaneous perturbation scheme for...