Perry de Valpine

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Maximum likelihood estimation and likelihood ratio tests for nonlinear, non-Gaussian state-space models require numerical integration for likelihood calculations. Several methods, including Monte Carlo (MC) expectation maximization, MC likelihood ratios, direct MC integration, and particle Ž lter likelihoods, are inefŽ cient for the motivating problem of(More)
Statistical characterization of past fire regimes is important for both the ecology and management of fire-prone ecosystems. Survival analysis—or fire frequency analysis as it is often called in the fire literature—has increasingly been used over the last few decades to examine fire interval distributions. These distributions can be generated from a variety(More)
Natural habitat may deliver ecosystem services to agriculture through the provision of natural enemies of agricultural pests. Natural or non-crop habitat has strongly positive effects on natural enemies in cropland, but the resulting impact on pests is not as well established. This study measured weekly natural enemy (syrphid fly larvae) and pest (cabbage(More)
To cite this article: Perry de Valpine, Daniel Turek, Christopher J. Paciorek, Clifford AndersonBergman, Duncan Temple Lang & Rastislav Bodik (2016): Programming with models: writing statistical algorithms for general model structures with NIMBLE, Journal of Computational and Graphical Statistics, DOI: 10.1080/10618600.2016.1172487 To link to this article:(More)
Flexible discrete-time per-capita-growth-rate models accommodating a variety of density-dependent relationships offer parsimonious explanations for the variation of population abundance through time. However, the accuracy of standard approaches to parameter estimation and confidence interval construction for such models has not been explored in a(More)
Hierarchical models include random effects or latent state variables. This class of models includes state–space models for population dynamics, which incorporate process and sampling variation, and models with random individual or year effects in capture–mark–recapture models, for example. This paper reviews methods for frequentist analysis of hierarchical(More)
Traditional Markov chain Monte Carlo (MCMC) sampling of hidden Markov models (HMMs) involves latent states underlying an imperfect observation process, and generates posterior samples for top-level parameters concurrently with nuisance latent variables. When potentially many HMMs are embedded within a hierarchical model, this can result in prohibitively(More)
Wildlife data gathered by different monitoring techniques are often combined to estimate animal density. However, methods to check whether different types of data provide consistent information (i.e., can information from one data type be used to predict responses in the other?) before combining them are lacking. We used generalized linear models and(More)