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Department of Statistics

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https://hdl.handle.net/10217/100518
These digital collections include theses, dissertations, and datasets from the Department of Statistics. Due to departmental name changes, materials from the following historical department are also included here: Mathematics and Statistics.

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Browsing Department of Statistics by Subject "Bayesian hierarchical modeling"

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    Integrated statistical models in ecology
    (Colorado State University. Libraries, 2023) Van Ee, Justin, author; Hooten, Mevin, advisor; Koslovsky, Matthew, advisor; Keller, Kayleigh, committee member; Kaplan, Andee, committee member; Bailey, Larissa, committee member
    The number of endangered and vulnerable species continues to grow globally as a result of habitat destruction, overharvesting, invasive species, and climate change. Understanding the drivers of population decline is pivotal for informing species conservation. Many datasets collected are restricted to a limited portion of the species range, may not include observations of other organisms in the community, or lack temporal breadth. When analyzed independently, these datasets often overlook drivers of population decline, muddle community responses to ecological threats, and poorly predict population trajectories. Over the last decade, thanks to efforts like The Long Term Ecological Research Network and National Ecological Observatory Network, citizen science surveys, and technological advances, ecological datasets that provide insights about collections of organisms or multiple characteristics of the same organism have become prevalent. The conglomerate of datasets has the potential to provide novel insights, improve predictive performance, and disentangle the contributions of confounded factors, but specifying joint models that assimilate all the available data sources is both intellectually daunting and computationally prohibitive. I develop methodology for specifying computationally efficient integrated models. I discuss datasets frequently collected in ecology, objectives common to many analyses, and the methodological challenges associated with specifying joint models in these contexts. I introduce a suite of model building and computational techniques I used to facilitate inference in three applied analyses of ecological data. In a case study of the joint mammalian response to the bark beetle epidemic in Colorado, I describe a restricted regression approach to deconfounding the effects of environmental factors and community structure on species distributions. I highlight that fitting certain joint species distribution models in a restricted parameterization improves sampling efficiency. To improve abundance estimates for a federally protected species, I specify an integrated model for analyzing independent aerial and ground surveys. I use a Markov melding approach to facilitate posterior inference and construct the joint distribution implied by the prior information, assumptions, and data expressed across a chain of submodels. I extend the integrated model by assimilating additional demographic surveys of the species that allow abundance estimates to be linked to annual variability in population vital rates. To reduce computation time, both models are fit using a multi-stage Markov chain Monte Carlo algorithm with parallelization. In each applied analysis, I uncover associations that would have been overlooked had the datasets been analyzed independently and improve predictive performance relative to models fit to individual datasets.