Publication

Submitted Papers

  • Group-constrained latent Dirichlet allocation for fashion item recommendation
    Kim, S., Lee, J., Choi, J., Cho, J., Park, K. and Chae, M.

  • Rates of convergence for nonparametric estimation of singular distributions using generative adversarial networks [arXiv]
    Chae, M.

  • A likelihood approach to nonparametric estimation of a singular distribution using deep generative models [arXiv]
    Chae, M., Kim, D., Kim, Y. and Lin, L.

  • Online learning for Dirichlet process mixture models via weakly conjugate approximation
    Jeong, K., Kim, Y. and Chae, M.

Publications

  • Adaptive Bayesian inference for current status data on a grid
    Chae, M.
    Bernoulli. (To appear)

  • Posterior asymptotics in Wasserstein metrics on the real line [arXiv]
    Chae, M., De Blasi, P. and Walker, S.
    Electronic Journal of Statistics. (2021). 15:3635-3677

  • Bayesian high-dimensional semi-parametric inference beyond sub-Gaussian errors
    Lee, K., Chae, M. and Lin, L.
    Journal of the Korean Statistical Society. (2021). 50(2):511-527

  • Wasserstein upper bounds of the total variation for smooth densities
    Chae, M. and Walker, S.
    Statistics & Probability Letters. (2020). 163:1-6

  • An EM-based iterative method for solving large sparse linear systems [arXiv]
    Chae, M. and Walker, S.
    Linear and Multilinear Algebra. (2020). 68(1):45–62

  • Bayesian sparse linear regression with unknown symmetric error [arXiv]
    Chae, M., Lin, L. and Dunson, D.
    Information and Inference. (2019). 8(3):621–653

  • The semi-parametric Bernstein-von Mises theorem for regression models with symmetric errors [arXiv]
    Chae, M., Kim, Y. and Kleijn, B.
    Statistica Sinica. (2019). 29(3):1465–1487

  • On an algorithm for solving Fredholm integrals of the first kind [arXiv]
    Chae, M., Martin, R. and Walker, S.
    Statistics and Computing. (2019). 29(4):645–654

  • Additive time-dependent hazard model with doubly truncated data
    Frank, G., Chae, M. and Kim, Y.
    Journal of the Korean Statistical Society. (2019). 48(2):179–193

  • Bayesian consistency for a nonparametric stationary Markov model
    Chae, M. and Walker, S.
    Bernoulli. (2019). 25(2):877–901

  • Convergence of an iterative algorithm to the nonparametric MLE of a mixing distribution [arXiv]
    Chae, M., Martin, R. and Walker, S.
    Statistics & Probability Letters. (2018). 140:142–146

  • A novel approach to Bayesian consistency
    Chae, M. and Walker, S.
    Electronic Journal of Statistics. (2017). 11(2):4723–4745

  • An online Gibbs sampler algorithm for hierarchical Dirichlet processes prior
    Kim, Y., Chae, M., Jeong, K., Kang, B. and Chung, H.
    In Proceedings of the ECML-PKDD Discovery Challenge Workshop. (2016). pp. 509–523

  • A closer look at the personality turnover relationship: criterion expansion, dark traits, and time
    Woo, S., Chae, M., Jebb, A. and Kim, Y.
    Journal of Management. (2016). 42(2):357–385

  • Beta processes and survival analysis
    Kim, Y. and Chae, M.
    The Korean Journal of Applied Statistics. (2014). 27(6):891–907 (Written in Korean)

  • Development of high-value traits of dairy cattle using survival analysis
    Jeong, K., Chae, M., Lee, S., Cho, K. and Kim, Y.
    Journal of the Korean Data Analysis Society. (2013). 15(5):2407–2416 (Written in Korean)

  • Documents recommendation using large citation data
    Chae, M., Kang, M. and Kim, Y.
    Journal of the Korean Data and Information Science Society. (2013). 24(5):999–1011 (Written in Korean)

  • A mixture of beta-Dirichlet processes prior for Bayesian analysis of event history data
    Chae, M., Weissbach, R., Cho, K. and Kim, Y.
    Journal of the Korean Statistical Society. (2013). 42(3):313–321