arXiv subjects

Staci Hepler

Publications and source records attributed to Staci Hepler.

3 recordsLinked to original sources

arXiv2026

The Impact of a Gridded Streamflow Measure on Drought Variation in the Conterminous United States

Models for droughts draw on a wide range of meteorological and hydrological inputs. Stakeholders classify droughts according to different purposes and priorities, and accordingly rely on different measures to explain and predict the onset of drought. While many meteorological inputs are available as gridded data products, hydrological streamflow measurements are often only available as point-referenced gauge measurements. This leads to an issue of misalignment for studies relying on both areal meteorological and point-referenced hydrological data. Such gauge data can also have notable spatial and/or temporal missingness. Many areas remain ungauged, and where gauges exist, equipment malfunctions cause temporal gaps. In this study, we document the value of an existing gridded streamflow measure for the conterminous United States, one which was specifically designed to match the spatio-temporal support of publicly available meteorological and ordinal drought measurements. We use this homogenized database to assess the relative importance of this streamflow measure in explaining US drought variability. This assessment first requires that we address autocorrelation and variability in the variance of observed droughts, two extensions to existing statistical methodology. After suitably controlling for both, our results show that the streamflow measure is often the most statistically important explanatory variable from among a wide set of meteorological variables. We explore the spatial variation and some drivers of this result.

stat.AP↗

arXiv2025

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought

High dimensional space-time data pose known computational challenges when fitting spatio-temporal models. Such data show dependence across several dimensions of space as well as in time, and can easily involve hundreds of thousands of observations. Many spatio-temporal models result in a dependence structure across all observations and can be fit only at a substantial computational cost, arising from dense matrix inversion, high dimensional parameter spaces, poor mixing in Markov Chain Monte Carlo, or the impossibility of utilizing parallel computing due to a lack of independence anywhere in the model fitting process. These computational challenges are exacerbated when the response variable is ordinal, and especially as the number of ordered categories grows. Some spatio-temporal models achieve computational feasibility for large datasets but only through overly restrictive model simplifications, which we seek to avoid here. In this paper we demonstrate a two-stage algorithm to fit a Bayesian spatio-temporal model to large datasets when the response variable is ordinal. The first stage models locations independently in space, capturing temporal dependence, and can be run in parallel. The second stage resamples from the first stage posterior distributions with an acceptance probability computed to impose spatial dependence from the full spatio-temporal model. The result is fast Bayesian inference which samples from the full spatio-temporal posterior and is computationally feasible even for large datasets. We quantify the substantial computational gains our approach achieves, and demonstrate the preservation of the posterior distribution as compared to the more costly single-stage model fit. We apply our approach to a large spatio-temporal drought dataset in the United States, a dataset too large for many existing spatio-temporal methods.

stat.ME↗

arXiv2018

A latent spatial factor approach for synthesizing opioid associated deaths and treatment admissions in Ohio counties

Background: Opioid misuse is a major public health issue in the United States and in particular Ohio. However, the burden of the epidemic is challenging to quantify as public health surveillance measures capture different aspects of the problem. Here we synthesize county-level death and treatment counts to compare the relative burden across counties and assess associations with social environmental covariates. Methods: We construct a generalized spatial factor model to jointly model death and treatment rates for each county. For each outcome, we specify a spatial rates parameterization for a Poisson regression model with spatially varying factor loadings. We use a conditional autoregressive model to account for spatial dependence within a Bayesian framework. Results: The estimated spatial factor was highest in the southern and southwestern counties of the state, representing a higher burden of the opioid epidemic. We found that relatively high rates of treatment contributed to the factor in the southern part of the state; whereas, relatively higher rates of death contributed in the southwest. The estimated factor was also positively associated with the proportion of residents aged 18-64 on disability and negatively associated with the proportion of residents reporting white race. Conclusions: We synthesized the information in the opioid associated death and treatment counts through a spatial factor model to estimate a latent factor representing the consensus between the two surveillance measures. We believe this framework provides a coherent approach to describe the epidemic while leveraging information from multiple surveillance measures.

stat.AP↗