• Accepted

  • Model specification test in a semiparametric regression model for longitudinal data

    Cho, H. and Kim, S. (2017) Journal of Multivariate Analysis, To appear.

    A nonparametric hypothesis test for heteroscedasticity in multiple regression models

    Zambom, Z.A. and Kim, S. (2017) Canadian Journal of Statistics, To appear.

    Lag selection and model validation in nonparametric autoregressive conditional heteroscedastic models

    Zambom, Z.A. and Kim, S. (2017) Journal of Statistical Planning and Inference, To appear.

    Marginal quantile regression accounting for association across multiple quantiles

    Cho, H., Kim, S. and Kim, M.O. (2017) Journal of Multivariate Analysis, 155, 334-343.

    A nonparametric hypothesis test for heteroscedasticity

    Kim, S. and Zambom, Z.A. (2016) Journal of Nonparametric Statistics, 28, 752-767.

    Hypothesis testing for ARCH models: a multiple quantile regressions approach

    Kim, S. (2015) Journal of Time Series Analysis, 36, 26-38.

    Nonparametric functional central limit theorem for time series with application to self-normalized confidence interval

    Kim, S., Zhao, Z. and Shao, X. (2015) Journal of Multivariate Analysis, 133, 277–290.

    Specification test for Markov models with measurement errors

    Kim, S. and Zhao, Z. (2014) Journal of Multivariate Analysis, 130, 118–133.

    Unified inference for sparse and dense longitudinal models

    Kim, S. and Zhao, Z. (2013) Biometrika, 100, 203–212.

  • Submitted (*: Students)

  • Initial severity-dependent longitudinal model in application to a randomized controlled trial of women with depression

    Kim, S., Cho, H. and Zhang X., submitted to Journal of the American Statistical Association.

    Efficient estimation for time-varying coefficient longitudinal models

    Kim, S., Zhao, Z and Xiao, Z., submitted to Journal of Statistical Planning and Inference.

    Partially linear quantile regression for longitudinal studies

    Kim, S. and Cho, H., submitted to Electronic Journal of Statistics.

    Predictive Modeling of Obesity Prevalence for the United States Population

    *Palma, D., Kim, S. and Miljkovic, T., submitted to North American Actuarial Journal.

  • In preparation

  • Quantile regression for locally stationary autoregressive model

    Kim, S., Zhao, Z. and Xu, Z.

    Nonparametric stationarity test for time series

    Kim, S. and Zambom, Z.A.

    Nonparametric estimation of bivariate rank-tracking probabilities in longitudinal studies

    Kim, S., Cho, H. and Wu, C.

    Health and Retirement Study Health and Retirement Study

    McLaughlin, S. and Kim, S.