Methods to compute prediction intervals: A review and new results

Q Tian, DJ Nordman, WQ Meeker - Statistical Science, 2022 - projecteuclid.org
Methods to Compute Prediction Intervals: A Review and New Results Page 1 Statistical Science
2022, Vol. 37, No. 4, 580–597 https://doi.org/10.1214/21-STS842 © Institute of Mathematical …

An international game of risk: Troop placement and major power competition

MD Nieman, C Martinez Machain… - The Journal of …, 2021 - journals.uchicago.edu
What strategies are behind major powers' decisions to deploy forces abroad? We argue that
major powers use noninvasion troop deployments to create, consolidate, and expand their …

A formal goodness-of-fit test for spatial binary Markov random field models

E Biswas, A Kaplan, MS Kaiser, DJ Nordman - Biometrics, 2024 - academic.oup.com
Binary spatial observations arise in environmental and ecological studies, where Markov
random field (MRF) models are often applied. Despite the prevalence and the long history of …

Modeling transitivity in local structure graph models

E Casleton, DJ Nordman, MS Kaiser - Sankhya A, 2021 - Springer
Abstract Local Structure Graph Models (LSGMs) describe network data by modeling, and
thereby controlling, the local structure of networks in a direct and interpretable manner …

Local structure graph models with higher‐order dependence

EM Casleton, DJ Nordman… - Canadian Journal of …, 2021 - Wiley Online Library
Local structure graph models (LSGMs) describe random graphs and networks as a Markov
random field (MRF)—each graph edge has a specified conditional distribution dependent on …

On the S-instability and degeneracy of discrete deep learning models

A Kaplan, DJ Nordman… - Information and Inference …, 2020 - academic.oup.com
A probability model exhibits instability if small changes in a data outcome result in large and,
often unanticipated, changes in probability. This instability is a property of the probability …

Simulating Markov random fields with a conclique-based Gibbs sampler

A Kaplan, MS Kaiser, SN Lahiri… - … of Computational and …, 2020 - Taylor & Francis
For spatial and network data, we consider models formed from a Markov random field (MRF)
structure and the specification of a conditional distribution for each observation. Fast …

A Local Structure Graph Model: Modeling Formation of Network Edges as a Function of Other Edges

OV Chyzh, MS Kaiser - Political Analysis, 2019 - cambridge.org
Localized network processes are central to the study of political science, whether in the
formation of political coalitions and voting blocs, balancing and bandwagoning, policy …

On advancing MCMC-based methods for Markovian data structures with applications to deep learning, simulation, and resampling

A Kaplan - 2017 - search.proquest.com
Abstract Markov chain Monte Carlo (MCMC) is a computational statistical approach for
numerically approximating distributional quantities useful for inference that might otherwise …

Análisis del movimiento de residentes en España mediante Redes Complejas

A Domínguez-Adame Palomo - 2021 - idus.us.es
Este trabajo tiene como objetivo el análisis del movimiento de residentes en España en el
periodo 2015-2019 a través de la metodología de Redes Complejas. Dicha metodología …