69 citations · 130 across the 29 of their papers we have counts for
20 papers · 1 filter
Making Recursive Bayesian Inference Robust
Myungsoo Yoo, Daniel Würzler Barreto, Mevin B. Hooten
While Bayesian inference has become increasingly popular with advances in computational resources, its algorithms can be computationally prohibitive and may not scale with large da…
Network knockoffs: controlling false discovery in dyadic space
Justin Van Ee, Yoichiro Kanno, Jacob Rash +1
Phenomena such as epidemiological processes, hydrologic systems, social platforms, utility services, and supply chains can be represented as topological networks. A central questio…
Recursive Adaptive Importance Sampling with Optimal Replenishment
Daniel Würzler Barreto, Mevin B. Hooten
Increased access to computing resources has led to the development of algorithms that can run efficiently on multi-core processing units or in distributed computing environments. I…
A multi-stage Bayesian approach to fit spatial point process models
Rachael Ren, Mevin B. Hooten, Toryn L. J. Schafer +4
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 fitt…
Spatial Hyperspheric Models for Compositional Data
Michael R. Schwob, Mevin B. Hooten, Nicholas M. Calzada +1
Compositional observations are an increasingly prevalent data source in spatial statistics. Analysis of such data is typically done on log-ratio transformations or via Dirichlet re…
Spatial Knockoff Bayesian Variable Selection in Genome-Wide Association Studies
Justin J. Van Ee, Diana Gamba, Jesse R. Lasky +2
High-dimensional variable selection has emerged as one of the prevailing statistical challenges in the big data revolution. Many variable selection methods have been adapted for id…
Composite Dyadic Models for Spatio-Temporal Data
Michael R Schwob, Mevin B Hooten, Vagheesh Narasimhan
Mechanistic statistical models are commonly used to study the flow of biological processes. For example, in landscape genetics, the aim is to infer spatial mechanisms that govern g…
A Unified Bayesian Framework for Modeling Measurement Error in Multinomial Data
Matthew D. Koslovsky, Andee Kaplan, Victoria A. Terranova +1
Measurement error in multinomial data is a well-known and well-studied inferential problem that is encountered in many fields, including engineering, biomedical and omics research,…
Geostatistical capture-recapture models
Mevin B Hooten, Michael R Schwob, Devin S Johnson +1
Methods for population estimation and inference have evolved over the past decade to allow for the incorporation of spatial information when using capture-recapture study designs.…
Dynamic Population Models with Temporal Preferential Sampling to Infer Phenology
Michael R. Schwob, Mevin B. Hooten, Travis McDevitt-Galles
To study population dynamics, ecologists and wildlife biologists use relative abundance data, which are often subject to temporal preferential sampling. Temporal preferential sampl…
Melding Wildlife Surveys to Improve Conservation Inference
Justin Van Ee, Christian Hagen, David Pavlacky +3
Integrated models are a popular tool for analyzing species of conservation concern. Species of conservation concern are often monitored by multiple entities that generate several d…
Multistage Hierarchical Capture-Recapture Models
Mevin B Hooten, Michael R Schwob, Devin S Johnson +1
Ecologists increasingly rely on Bayesian methods to fit capture-recapture models. Capture-recapture models are used to estimate abundance while accounting for imperfect detectabili…
Greater Than the Sum of its Parts: Computationally Flexible Bayesian Hierarchical Modeling
Devin S. Johnson, Brian M. Brost, Mevin B. Hooten
We propose a multistage method for making inference at all levels of a Bayesian hierarchical model (BHM) using natural data partitions to increase efficiency by allowing computatio…
Animal Movement Models with Mechanistic Selection Functions
Mevin B. Hooten, Xinyi Lu, Martha J. Garlick +1
A suite of statistical methods are used to study animal movement. Most of these methods treat animal telemetry data in one of three ways: as discrete processes, as continuous proce…
Hierarchical approaches for flexible and interpretable binary regression models
Henry R. Scharf, Xinyi Lu, Perry J. Williams +1
Binary regression models are ubiquitous in virtually every scientific field. Frequently, traditional generalized linear models fail to capture the variability in the probability su…
Predicting paleoclimate from compositional data using multivariate Gaussian process inverse prediction
John R. Tipton, Mevin B. Hooten, Connor Nolan +2
Multivariate compositional count data arise in many applications including ecology, microbiology, genetics, and paleoclimate. A frequent question in the analysis of multivariate co…
Making Recursive Bayesian Inference Accessible
Mevin B. Hooten, Devin S. Johnson, Brian M. Brost
Bayesian models provide recursive inference naturally because they can formally reconcile new data and existing scientific information. However, popular use of Bayesian methods oft…
Imputation Approaches for Animal Movement Modeling
Henry R. Scharf, Mevin B. Hooten, Devin S. Johnson
The analysis of telemetry data is common in animal ecological studies. While the collection of telemetry data for individual animals has improved dramatically, the methods to prope…
Process convolution approaches for modeling interacting trajectories
Henry R. Scharf, Mevin B. Hooten, Devin S. Johnson +1
Gaussian processes are a fundamental statistical tool used in a wide range of applications. In the spatio-temporal setting, several families of covariance functions exist to accomm…
Basis Function Models for Animal Movement
Mevin B. Hooten, Devin S. Johnson
Advances in satellite-based data collection techniques have served as a catalyst for new statistical methodology to analyze these data. In wildlife ecological studies, satellite-ba…