Seonghyun Jeong

Works (6)

Updated: July 5th, 2023 15:36

2021 journal article

Unified Bayesian theory of sparse linear regression with nuisance parameters

ELECTRONIC JOURNAL OF STATISTICS, 15(1), 3040–3111.

By: S. Jeong* & S. Ghosal  n

author keywords: Bernstein-von Mises theorems; High-dimensional regression; Model selection consistency; Posterior contraction rates; Sparse priors
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Source: Web Of Science
Added: June 28, 2021

2020 journal article

Bayesian linear regression for multivariate responses under group sparsity

BERNOULLI, 26(3), 2353–2382.

By: B. Ning *, S. Jeong* & S. Ghosal  n

author keywords: Bayesian variable selection; covariance matrix; group sparsity; multivariate linear regression; posterior contraction rate; Renyi divergence; spike-and-slab prior
TL;DR: The posterior contraction rate is derived using the general theory by constructing a suitable test from the first principle using moment bounds for certain likelihood ratios, which leads to posterior concentration around the truth with respect to the average Renyi divergence of order 1/2. (via Semantic Scholar)
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Source: Web Of Science
Added: May 18, 2020

2020 journal article

Posterior contraction in sparse generalized linear models

BIOMETRIKA, 108(2), 367–379.

By: S. Jeong* & S. Ghosal  n

author keywords: Fractional posterior; Generalized linear model; High-dimensional regression; Posterior contraction rate; Sparsity-inducing prior
TL;DR: This work studies posterior contraction rates in sparse high-dimensional generalized linear models using priors incorporating sparsity, and shows that Bayesian methods achieve convergence properties analogous to lasso-type procedures. (via Semantic Scholar)
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Source: Web Of Science
Added: July 12, 2021

2019 journal article

Emerging procurement technology: data analytics and cognitive analytics

INTERNATIONAL JOURNAL OF PHYSICAL DISTRIBUTION & LOGISTICS MANAGEMENT, 49(10), 972–1002.

By: R. Handfield n , S. Jeong* & T. Choi *

Contributors: R. Handfield n , S. Jeong* & T. Choi *

TL;DR: The procurement analytics landscape developed in this research suggests that the authors will continue to see major shifts in the sourcing and supply chain technology environment in the next five years and detailed implementation strategies of emerging procurement technologies are suggested, contributing to the existing body of the literature and industry reports. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries, ORCID
Added: December 16, 2019

2017 journal article

Analysis of Poisson varying-coefficient models with autoregression

Statistics, 52(1), 34–49.

By: T. Park * & S. Jeong n

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Source: NC State University Libraries
Added: August 6, 2018

2017 journal article

Analysis of binary longitudinal data with time-varying effects

COMPUTATIONAL STATISTICS & DATA ANALYSIS, 112, 145–153.

By: S. Jeong n, M. Park * & T. Park *

author keywords: Longitudinal data; Probit mixed model; Nonparametric regression; Partial collapse; Repeated measures
TL;DR: A Bayesian method is proposed for the analysis of binary longitudinal data with time-varying regression coefficients and random effects to account for nonlinear subject-specific effects over time as well as between-subject variation. (via Semantic Scholar)
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Source: Web Of Science
Added: August 6, 2018

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