arXiv subjects

Jong Hee Park

Publications and source records attributed to Jong Hee Park.

5 recordsLinked to original sources

arXiv2026

Euclidean Ideal Point Estimation From Roll-Call Data via Distance-Based Bipartite Network Models

Conventional ideal point models rely on Gaussian or quadratic utility functions that violate the triangle inequality, producing non-metric distances that complicate geometric interpretation and undermine clustering and dispersion-based analyses. We introduce a distance-based alternative that adapts the Latent Space Item Response Model (LSIRM) to roll-call data, treating legislators and bills as nodes in a bipartite network jointly embedded in a Euclidean metric space. Through controlled simulations, Euclidean LSIRM consistently recovers latent coalition structure with superior cluster separation relative to existing methods. Applied to the 118th U.S. House, the model provides competitive predictive performance while yielding bill embeddings that clarify cross-cutting issue alignments. The results show that restoring metric structure to ideal point estimation provides a clearer and more coherent inference about party cohesion, factional divisions, and multidimensional legislative behavior.

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arXiv2026

Uncertainty-Aware Ideal Point Estimation via Variational EM

Roll-call data analysis aims to estimate legislators' ideal points and quantify the associated uncertainty. Existing approaches either rely on Bayesian methods implemented via Markov chain Monte Carlo sampling or focus primarily on point estimation, with uncertainty typically assessed through resampling procedures such as the bootstrap. Consequently, the computational burden of these approaches can become substantial when applied to large roll-call datasets. To address this challenge, we propose a computationally efficient likelihood method for estimating ideal points and their standard errors. Leveraging the Pólya--Gamma identity, we develop a variational expectation--maximization algorithm for estimating ideal points and introduce a variational Louis' method to approximate the observed Fisher information for standard error estimation. Numerical studies and applications to U.S. congressional roll-call data demonstrate that the proposed method produces accurate ideal point estimates and reliable standard errors while being substantially more computationally efficient than existing approaches.

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arXiv2025

$\ell_0$-Regularized Item Response Theory Model for Robust Ideal Point Estimation

Ideal point estimation methods face a significant challenge when legislators engage in protest voting -- strategically voting against their party to express dissatisfaction. Such votes introduce attenuation bias, making ideologically extreme legislators appear artificially moderate. We propose a novel statistical framework that extends the fast EM-based estimation approach of \cite{Imai2016} using $\ell_0$ regularization method to handle protest votes. Through simulation studies, we demonstrate that our proposed method maintains estimation accuracy even with high proportions of protest votes, while being substantially faster than MCMC-based methods. Applying our method to the 116th and 117th U.S. House of Representatives, we successfully recover the extreme liberal positions of ``the Squad'', whose protest votes had caused conventional methods to misclassify them as moderates. While conventional methods rank Ocasio-Cortez as more conservative than 69\% of Democrats, our method places her firmly in the progressive wing, aligning with her documented policy positions. This approach provides both robust ideal point estimates and systematic identification of protest votes, facilitating deeper analysis of strategic voting behavior in legislatures.

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arXiv2018

Twists and Turns in the US-North Korea Dialogue: Key Figure Dynamic Network Analysis using News Articles

In this paper, we present a method for analyzing a dynamic network of key figures in the U.S.-North Korea relations during the first two quarters of 2018. Our method constructs key figure networks from U.S. news articles on North Korean issues by taking co-occurrence of people's names in an article as a domain-relevant social link. We call a group of people that co-occur repeatedly in the same domain (news articles on North Korean issues in our case) "key figures" and their social networks "key figure networks." We analyze block-structure changes of key figure networks in the U.S.-North Korea relations using a Bayesian hidden Markov multilinear tensor model. The results of our analysis show that block structure changes in the key figure network in the U.S.-North Korea relations predict important game-changing moments in the U.S.-North Korea relations in the first two quarters of 2018.

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arXiv2018

Detecting Structural Changes in Longitudinal Network Data

Dynamic modeling of longitudinal networks has been an increasingly important topic in applied research. While longitudinal network data commonly exhibit dramatic changes in its structures, existing methods have largely focused on modeling smooth topological changes over time. In this paper, we develop a hidden Markov multilinear tensor model (HMTM) that combines the multilinear tensor regression model (Hoff 2011) with a hidden Markov model using Bayesian inference. We model changes in network structure as shifts in discrete states yielding particular sets of network generating parameters. Our simulation results demonstrate that the proposed method correctly detects the number, locations, and types of changes in latent node characteristics. We apply the proposed method to international military alliance networks to find structural changes in the coalition structure among nations.

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