@article{jeong_ghosal_2021, title={Posterior contraction in sparse generalized linear models}, volume={108}, ISSN={["1464-3510"]}, DOI={10.1093/biomet/asaa074}, abstractNote={Summary}, number={2}, journal={BIOMETRIKA}, author={Jeong, Seonghyun and Ghosal, Subhashis}, year={2021}, month={Jun}, pages={367–379} }
 @article{jeong_ghosal_2021, title={Unified Bayesian theory of sparse linear regression with nuisance parameters}, volume={15}, ISSN={["1935-7524"]}, DOI={10.1214/21-EJS1855}, abstractNote={We study frequentist asymptotic properties of Bayesian procedures for high-dimensional Gaussian sparse regression when unknown nuisance parameters are involved. Nuisance parameters can be finite-, high-, or infinite-dimensional. A mixture of point masses at zero and continuous distributions is used for the prior distribution on sparse regression coefficients, and appropriate prior distributions are used for nuisance parameters. The optimal posterior contraction of sparse regression coefficients, hampered by the presence of nuisance parameters, is also examined and discussed. It is shown that the procedure yields strong model selection consistency. A Bernstein-von Mises-type theorem for sparse regression coefficients is also obtained for uncertainty quantification through credible sets with guaranteed frequentist coverage. Asymptotic properties of numerous examples are investigated using the theories developed in this study.}, number={1}, journal={ELECTRONIC JOURNAL OF STATISTICS}, author={Jeong, Seonghyun and Ghosal, Subhashis}, year={2021}, pages={3040–3111} }
 @article{ning_jeong_ghosal_2020, title={Bayesian linear regression for multivariate responses under group sparsity}, volume={26}, ISSN={["1573-9759"]}, DOI={10.3150/20-BEJ1198}, abstractNote={We study frequentist properties of a Bayesian high-dimensional multivariate linear regression model with correlated responses. The predictors are separated into many groups and the group structure is pre-determined. Two features of the model are unique: (i) group sparsity is imposed on the predictors. (ii) the covariance matrix is unknown and its dimensions can also be high. We choose a product of independent spike-and-slab priors on the regression coefficients and a new prior on the covariance matrix based on its eigendecomposition. Each spike-and-slab prior is a mixture of a point mass at zero and a multivariate density involving a $\ell_{2,1}$-norm. We first obtain the posterior contraction rate, the bounds on the effective dimension of the model with high posterior probabilities. We then show that the multivariate regression coefficients can be recovered under certain compatibility conditions. Finally, we quantify the uncertainty for the regression coefficients with frequentist validity through a Bernstein-von Mises type theorem. The result leads to selection consistency for the Bayesian method. We derive the posterior contraction rate using the general theory by constructing a suitable test from the first principle using moment bounds for certain likelihood ratios. This leads to posterior concentration around the truth with respect to the average Renyi divergence of order 1/2. This technique of obtaining the required tests for posterior contraction rate could be useful in many other problems.}, number={3}, journal={BERNOULLI}, author={Ning, Bo and Jeong, Seonghyun and Ghosal, Subhashis}, year={2020}, month={Aug}, pages={2353–2382} }
 @article{handfield_jeong_choi_2019, title={Emerging procurement technology: data analytics and cognitive analytics}, volume={49}, ISBN={1758-664X}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85066024508&partnerID=MN8TOARS}, DOI={10.1108/IJPDLM-11-2017-0348}, abstractNote={PurposeThe purpose of this paper is to elucidate the emerging landscape of procurement analytics. This paper focuses on the following questions: what are the current and future state of procurement analytics?; what changes in the procurement process will be required to enable integration of analytical solutions?; and what future areas of research arise when considering the future state of procurement analytics?}, number={10}, journal={INTERNATIONAL JOURNAL OF PHYSICAL DISTRIBUTION & LOGISTICS MANAGEMENT}, author={Handfield, Robert and Jeong, Seongkyoon and Choi, Thomas}, year={2019}, pages={972–1002} }
 @article{park_jeong_2018, title={Analysis of Poisson varying-coefficient models with autoregression}, volume={52}, DOI={10.1080/02331888.2017.1353514}, abstractNote={ABSTRACT In the regression analysis of time series of event counts, it is of interest to account for serial dependence that is likely to be present among such data as well as a nonlinear interaction between the expected event counts and predictors as a function of some underlying variables. We thus develop a Poisson autoregressive varying-coefficient model, which introduces autocorrelation through a latent process and allows regression coefficients to nonparametrically vary as a function of the underlying variables. The nonparametric functions for varying regression coefficients are estimated with data-driven basis selection, thereby avoiding overfitting and adapting to curvature variation. An efficient posterior sampling scheme is devised to analyse the proposed model. The proposed methodology is illustrated using simulated data and daily homicide data in Cali, Colombia.}, number={1}, journal={Statistics}, author={Park, T. and Jeong, S.}, year={2018}, pages={34–49} }
 @article{jeong_park_park_2017, title={Analysis of binary longitudinal data with time-varying effects}, volume={112}, ISSN={["1872-7352"]}, DOI={10.1016/j.csda.2017.03.007}, abstractNote={This paper considers the analysis of longitudinal data where a binary response variable is observed repeatedly for each subject over time. In analyzing such data, regression coefficients are commonly assumed constant over time, which may not properly account for the time-varying effects of some subject characteristics on a sequence of binary outcomes. This paper proposes a Bayesian method 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. The proposed method facilitates posterior computation via the method of partial collapse and accommodates spatially inhomogeneous smoothness of nonparametric functions without overfitting via a basis search technique. The proposed method is illustrated with a simulated study and the binary longitudinal data from the German socioeconomic panel study.}, journal={COMPUTATIONAL STATISTICS & DATA ANALYSIS}, author={Jeong, Seonghyun and Park, Minjae and Park, Taeyoung}, year={2017}, month={Aug}, pages={145–153} }