arXiv subjects

Toryn L. J. Schafer

Publications and source records attributed to Toryn L. J. Schafer.

10 recordsLinked to original sources

arXiv2026

A multi-stage Bayesian approach to fit spatial point process models

Spatial point process (SPP) models are commonly used to analyze point pattern data in many fields, including presence-only data in ecology. Existing exact Bayesian methods for fitting these models are computationally expensive because they require approximating an intractable integral each time parameters are updated and often involve algorithm supervision (i.e., tuning in the Bayesian setting). We propose a flexible, efficient, and exact multi-stage recursive Bayesian approach to fitting SPP models that leverages parallel computing resources to obtain realizations from the joint posterior, which can then be used to obtain inference on derived quantities. We outline potential extensions, including a framework for analyzing study designs with compact observation windows and a neural network basis expansion for increased model flexibility. We demonstrate this approach and its extensions using a simulation study and analyze data from aerial imagery surveys to improve our understanding of spatially explicit abundance of harbor seal (Phoca vitulina) pups in Johns Hopkins Inlet, a protected tidewater glacial fjord in Glacier Bay National Park, Alaska.

stat.ME↗

arXiv2024

Trend and Variance Adaptive Bayesian Changepoint Analysis & Local Outlier Scoring

We adaptively estimate both changepoints and local outlier processes in a Bayesian dynamic linear model with global-local shrinkage priors in a novel model we call Adaptive Bayesian Changepoints with Outliers (ABCO). We utilize a state-space approach to identify a dynamic signal in the presence of outliers and measurement error with stochastic volatility. We find that global state equation parameters are inadequate for most real applications and we include local parameters to track noise at each time-step. This setup provides a flexible framework to detect unspecified changepoints in complex series, such as those with large interruptions in local trends, with robustness to outliers and heteroskedastic noise. Finally, we compare our algorithm against several alternatives to demonstrate its efficacy in diverse simulation scenarios and two empirical examples on the U.S. economy.

stat.ME↗

arXiv2024

Drift vs Shift: Decoupling Trends and Changepoint Analysis

We introduce a new approach for decoupling trends (drift) and changepoints (shifts) in time series. Our locally adaptive model-based approach for robustly decoupling combines Bayesian trend filtering and machine learning based regularization. An over-parameterized Bayesian dynamic linear model (DLM) is first applied to characterize drift. Then a weighted penalized likelihood estimator is paired with the estimated DLM posterior distribution to identify shifts. We show how Bayesian DLMs specified with so-called shrinkage priors can provide smooth estimates of underlying trends in the presence of complex noise components. However, their inability to shrink exactly to zero inhibits direct changepoint detection. In contrast, penalized likelihood methods are highly effective in locating changepoints. However, they require data with simple patterns in both signal and noise. The proposed decoupling approach combines the strengths of both, i.e. the flexibility of Bayesian DLMs with the hard thresholding property of penalized likelihood estimators, to provide changepoint analysis in complex, modern settings. The proposed framework is outlier robust and can identify a variety of changes, including in mean and slope. It is also easily extended for analysis of parameter shifts in time-varying parameter models like dynamic regressions. We illustrate the flexibility and contrast the performance and robustness of our approach with several alternative methods across a wide range of simulations and application examples.

stat.ME↗

arXiv2023

Locally Adaptive Shrinkage Priors for Trends and Breaks in Count Time Series

Non-stationary count time series characterized by features such as abrupt changes and fluctuations about the trend arise in many scientific domains including biophysics, ecology, energy, epidemiology, and social science domains. Current approaches for integer-valued time series lack the flexibility to capture local transient features while more flexible models for continuous data types are inadequate for universal applications to integer-valued responses such as settings with small counts. We present a modeling framework, the negative binomial Bayesian trend filter (NB-BTF), that offers an adaptive model-based solution to capturing multiscale features with valid integer-valued inference for trend filtering. The framework is a hierarchical Bayesian model with a dynamic global-local shrinkage process. The flexibility of the global-local process allows for the necessary local regularization while the temporal dependence induces a locally smooth trend. In simulation, the NB-BTF outperforms a number of alternative trend filtering methods. Then, we demonstrate the method on weekly power outage frequency in Massachusetts townships. Power outage frequency is characterized by a nominal low level with occasional spikes. These illustrations show the estimation of a smooth, non-stationary trend with adequate uncertainty quantification.

stat.ME↗

arXiv2022

Role of Variable Renewable Energy Penetration on Electricity Price and its Volatility Across Independent System Operators in the United States

The U.S. electrical grid has undergone substantial transformation with increased penetration of wind and solar -- forms of variable renewable energy (VRE). Despite the benefits of VRE for decarbonization, it has garnered some controversy for inducing unwanted effects in regional electricity markets. In this study, the role of VRE penetration is examined on the system electricity price and price volatility based on hourly, real-time, historical data from six Independent System Operators (ISOs) in the U.S. using quantile and skew t-distribution regressions. After correcting for temporal effects, we found an increase in VRE penetration is associated with decrease in system electricity price in all ISOs studied. The increase in VRE penetration is associated with decrease in temporal price volatility in five out of six ISOs studied. The relationships are non-linear. These results are consistent with the modern portfolio theory where diverse volatile assets may lead to more stable and less risky portfolios.

q-fin.GN↗

arXiv2022

Bayesian Inverse Reinforcement Learning for Collective Animal Movement

Agent-based methods allow for defining simple rules that generate complex group behaviors. The governing rules of such models are typically set a priori and parameters are tuned from observed behavior trajectories. Instead of making simplifying assumptions across all anticipated scenarios, inverse reinforcement learning provides inference on the short-term (local) rules governing long term behavior policies by using properties of a Markov decision process. We use the computationally efficient linearly-solvable Markov decision process to learn the local rules governing collective movement for a simulation of the self propelled-particle (SPP) model and a data application for a captive guppy population. The estimation of the behavioral decision costs is done in a Bayesian framework with basis function smoothing. We recover the true costs in the SPP simulation and find the guppies value collective movement more than targeted movement toward shelter.

cs.LG↗

arXiv2021

Analysis of animal-related electric outages using species distribution models and community science data

Animal-related outages (AROs) are a prevalent form of outages in electrical distribution systems. Animal-infrastructure interactions vary across focal species and regions, underlining the need to study the animal-outage relationship in more species and diverse systems. Animal activity has been used as an indicator of reliability in the electrical grid system and to describe temporal patterns in AROs. However, these ARO models have been limited by a lack of available estimates of species activity, instead approximating activity based on seasonal and weather patterns in animal-related outage records and characteristics of broad taxonomic groups, e.g., squirrels. We highlight publicly available resources to fill the ecological data gap that is limiting joint analyses between ecology and energy sectors. Species distribution models (SDMs), a common technique to model the distribution of a species across geographic space and time, paired with data sourced from eBird, a community science database for bird observations, provided us with species-specific estimates of activity to model spatio-temporal patterns of AROs. These flexible, species-specific estimates can allow future animal-indicators of grid reliability to be investigated in more diverse regions and ecological communities, providing a better understanding of the variation that exists in animal-outage relationship. AROs were best modeled by accounting for multiple outage-prone species activity patterns and their unique relationships with seasonality and habitat availability. Different species were important for modeling outages in different landscapes and seasons depending on their distribution and migration behavior. We recommend that future models of AROs include species-specific activity data that account for the diverse spectrum of spatio-temporal activity patterns that outage-prone animals exhibit.

q-bio.OT↗

arXiv2021

Critical Risk Indicators (CRIs) for the electric power grid: A survey and discussion of interconnected effects

The electric power grid is a critical societal resource connecting multiple infrastructural domains such as agriculture, transportation, and manufacturing. The electrical grid as an infrastructure is shaped by human activity and public policy in terms of demand and supply requirements. Further, the grid is subject to changes and stresses due to solar weather, climate, hydrology, and ecology. The emerging interconnected and complex network dependencies make such interactions increasingly dynamic causing potentially large swings, thus presenting new challenges to manage the coupled human-natural system. This paper provides a survey of models and methods that seek to explore the significant interconnected impact of the electric power grid and interdependent domains. We also provide relevant critical risk indicators (CRIs) across diverse domains that may influence electric power grid risks, including climate, ecology, hydrology, finance, space weather, and agriculture. We discuss the convergence of indicators from individual domains to explore possible systemic risk, i.e., holistic risk arising from cross-domains interconnections. Our study provides an important first step towards data-driven analysis and predictive modeling of risks in the coupled interconnected systems. Further, we propose a compositional approach to risk assessment that incorporates diverse domain expertise and information, data science, and computer science to identify domain-specific CRIs and their union in systemic risk indicators.

stat.AP↗

arXiv2020

A Bayesian Markov model with Pólya-Gamma sampling for estimating individual behavior transition probabilities from accelerometer classifications

The use of accelerometers in wildlife tracking provides a fine-scale data source for understanding animal behavior and decision-making. Current methods in movement ecology focus on behavior as a driver of movement mechanisms. Our Markov model is a flexible and efficient method for inference related to effects on behavior that considers dependence between current and past behaviors. We applied this model to behavior data from six greater white-fronted geese (Anser albifrons frontalis) during spring migration in mid-continent North America and considered likely drivers of behavior, including habitat, weather and time of day effects. We modeled the transitions between flying, feeding, stationary and walking behavior states using a first-order Bayesian Markov model. We introduced Pólya-Gamma latent variables for automatic sampling of the covariate coefficients from the posterior distribution and we calculated the odds ratios from the posterior samples. Our model provides a unifying framework for including both acceleration and Global Positioning System data. We found significant differences in behavioral transition rates among habitat types, diurnal behavior and behavioral changes due to weather. Our model provides straightforward inference of behavioral time allocation across used habitats, which is not amenable in activity budget or resource selection frameworks.

stat.AP↗

arXiv2020

Distributed lag models to identify the cumulative effects of training and recovery in athletes using multivariate ordinal wellness data

Subjective wellness data can provide important information on the well-being of athletes and be used to maximize player performance and detect and prevent against injury. Wellness data, which are often ordinal and multivariate, include metrics relating to the physical, mental, and emotional status of the athlete. Training and recovery can have significant short- and long-term effects on athlete wellness, and these effects can vary across individual. We develop a joint multivariate latent factor model for ordinal response data to investigate the effects of training and recovery on athlete wellness. We use a latent factor distributed lag model to capture the cumulative effects of training and recovery through time. Current efforts using subjective wellness data have averaged over these metrics to create a univariate summary of wellness, however this approach can mask important information in the data. Our multivariate model leverages each ordinal variable and can be used to identify the relative importance of each in monitoring athlete wellness. The model is applied to athlete daily wellness, training, and recovery data collected across two Major League Soccer seasons.

stat.AP↗