activity
20122025
most citedContinuous-time discrete-space models for animal movement

69 citations · 107 across the 15 of their papers we have counts for

collaborators

16 papers

stat.AP2025

An Integrated Time-Varying Ornstein-Uhlenbeck Process for Jointly Modeling Individual and Population-Level Movement of Golden Eagles

Michael L. Shull, Ephraim M. Hanks, James C. Russell +2

With technological advancements, the quantity and quality of animal movement data have increased greatly. Currently, no movement model can be used to describe full-year data from m…

stat.AP2024★ 2 cited

Multiscale modelling of animal movement with persistent dynamics

Théo Michelot, Ephraim M. Hanks

Wild animals are commonly fitted with trackers that record their position through time, and statistical models for tracking data broadly fall into two categories: models focused on…

stat.AP2023

Modeling First Arrival of Migratory Birds using a Hierarchical Max-infinitely Divisible Process

Dhanushi A. Wijeyakulasuriya, Ephraim M. Hanks, Benjamin A. Shaby

Humans have recorded the arrival dates of migratory birds for millennia, searching for trends and patterns. As the first arrival among individuals in a species is the realized tail…

stat.ME2021

A New Framework for Inference on Markov Population Models

Adam Walder, Ephraim M. Hanks

In this work we construct a joint Gaussian likelihood for approximate inference on Markov population models. We demonstrate that Markov population models can be approximated by a s…

stat.ME2020

A Mechanistic Model of Annual Sulfate Concentrations in the United States

Nathan B. Wikle, Ephraim M. Hanks, Lucas R. F. Henneman +1

We develop a mechanistic model to analyze the impact of sulfur dioxide emissions from coal-fired power plants on average sulfate concentrations in the central United States. A mult…

stat.AP2020

Privacy for Spatial Point Process Data

Adam Walder, Ephraim M. Hanks, Aleksandra Slavković

In this work we develop methods for privatizing spatial location data, such as spatial locations of individual disease cases. We propose two novel Bayesian methods for generating s…