Semiparametric mixed-scale models using shared Bayesian forests

This paper demonstrates the advantages of sharing information about unknown features of covariates across multiple model components in various nonparametric regression problems including multivariate, heteroscedastic, and semicontinuous responses. In this paper, we present a methodology which allows for information to be shared nonparametrically across various model components using Bayesian sum-of-tree models. Our simulation results demonstrate that sharing of information across related model components is often very beneficial, particularly in sparse high-dimensional problems in which variable selection must be conducted. We illustrate our methodology by analyzing medical expenditure data from the Medical Expenditure Panel Survey (MEPS). To facilitate the Bayesian nonparametric regression analysis, we develop two novel models for analyzing the MEPS data using Bayesian additive regression trees—a heteroskedastic log-normal hurdle model with a “shrink-toward-homoskedasticity” prior and a gamma hurdle model.     

On sampling procedures in population and community ecology

In this paper we emphasize that sampling decisions in population and community ecology are context dependent. Thus, the selection of an appropriate sampling procedure should follow directly from considerations of the objectives of an investigation. We recognize eight sampling alternatives, which arise as a result of three basic dichotomies: parameter estimation versus pattern detection, univariate versus multivariate, and a discrete versus continuous sampling universe. These eight alternative sampling procedures are discussed as they relate to decisions regarding the required empirical sample size, the selection or arrangement of sampling units, and plot size and shape. Our results indicate that the decision-making process in sampling must be viewed as a flexible exercise, dictated not by generalized recommendations but by specific objectives: there is no panacea in ecological sampling. We also point to a number of unresolved sampling problems in ecology.     

Life history and the fitness consequences of imperfect information

The acquisition of information incurs costs in time, energy, exposure to predation, and/or lost opportunity. Without information, however, animals will be unable to assess the costs and benefits of decisions. Obtaining perfect information may be impossible, but how close to perfect do animals need assessments of ecological factors, such as predation risk, before estimation errors affect fitness? A recent article suggested that animals should be tolerant to imperfect information about predation risk, possibly relying on simple rules of thumb. Using a dynamic state variable approach, we examine some of the assumptions underlying this work, and show that tolerance towards imperfect information is dependent on life-history strategy. By changing the relationship between energy and fitness, assumptions about life-history strategies can be modified. Calculations show that tolerance to imperfect information is sensitive to these assumptions with some life histories leading to overestimation, while other life histories result in underestimation. One consistent effect across life histories is that animals with a higher rate of increase in fitness with respect to energy should show greater tolerance to imperfect information.