Publications: (* marks advisees of Huang)

 

o   Liu*, Q., Huang X., and Zhou H. (2024). The Flexible Gumbel Distribution: A New Model for Inference about the Mode. Stats 7, 317--332

o    Liu*, Q. and Huang X. (2024). Parametric modal regression with error in covariates. Biometrical Journal 66: 2200348

o    Yu*, Z. and Huang X. (2024). A new parameterization for elliptically symmetric angular Gaussian distributions of arbitrary dimension. Electronic Journal of Statistics 18, 30134

o   Zhang, H., Huang, X., and Arshad, H. (2023). Comparing dependent undirected Gaussian networks. Bayesian Analysis 18, 1341-1366.

o   Zhou, H. and Huang, X. (2022). Bayesian beta regression for bounded responses with unknown supports. Computational Statistics and Data Analysis 167, 107345.

o   Huang, X. and Zhang, H. (2021). Tests for differential Gaussian Bayesian networks based on quadratic inference functions. Computational Statistics and Data Analysis 159, 107209.

o   Huang, X. and Zhang, H. (2021). Corrected score methods for estimating Bayesian networks with error-prone nodes. Statistics in Medicine. 40, 2692--2712.

o   Zhang, H., Huang, X., Han, S., Rezwan, F., Karmaus, W., Arshad, H, and Holloway, J. (2021). Gaussian Bayesian network comparisons with graph ordering unknown. Computational Statistics and Data Analysis. 157, 107156.

o   Wang, D., Mou, X., Li*, X., and Huang, X. (2020). Local polynomial regression for pooled response data. Journal of Nonparametric Statistics 32, 814--837.

o   Zhou, H. and Huang, X. (2020). Parametric mode regression for bounded responses. Biometrical Journal 61, 1791-809.

o   Huang, X. (2020). Improved wrong-model inference for generalized linear models for binary responses in the presence of link misspecification. Statistical Methods & Applications. DOI: 10.1007/s10260-020-00529-3

o   Huang, X. and Zhou, H. (2020). Conditional density estimation with covariate measurement error. Electronic Journal of Statistics 14, 970--1023.

o   Li*, X. and Huang, X. (2019) Linear mode regression with covariate measurement error. Canadian Journal of Statistics 47, 262-280.

o   Yu*, S. and Huang, X. (2019) Link misspecification in generalized linear mixed models with a random intercept for binary responses. TEST 28, 827-843.

o   Zhou, H. and Huang, X. (2019). Bandwidth selection for nonparametric modal regression. Communications in Statistics - Simulation and Computation 48, 968-984.

o   Huang, X. and Warasi, M. (2017). Maximum likelihood estimators in regression models for error-prone group testing data. Scandinavian Journal of Statistics 44, 918-931.

o   Yu*, S. and Huang, X. (2017). Random-intercept misspecification in generalized linear mixed models for binary responses. Statistical Methods and Applications 26333-359.

o   Huang, X. and Zhou, H. (2017). An alternative local polynomial estimator for the errors-in-variables problem. Journal of Nonparametric Statistics 29, 301-325.

o   Huang, X. (2017). Semi-nonparametric smooth isotonic regression. Communications in Statistics, Theory and Methods. 46, 10071-10087.

o   Zhou, H. and Huang, X. (2016). Nonparametric modal regression in the presence of measurement error. Electronic Journal of Statistics 10, 3579-3620.

o   Wen*, C., Dryden, I. and Huang, X. (2016). Bayesian registration of functions and curves. Bayesian Analysis 11, 447-475.

o   Zhang, H., Huang, X., Gan, J., Karmaus W., and Sabo-Attwood, T. (2016). A two-component G-prior for variable selection. Bayesian Analysis 11, 353-380.

o   Huang, X. (2015). Dual model misspecification in generalized linear models with error in variables. Chapter 1 in New Developments in Statistical Modeling, Inference and Application, edited by Jin, Z., Liu, M., and Luo, X.. Springer.

o   Du*, J., Dryden, I., and Huang, X. (2015). Size and shape analysis of error-prone shape Data. Journal of the American Statistical Association 110, 368-379.

o   Huang, X. and Zhang, H. (2013). Variable selection in linear measurement error models via penalized score functions. Journal of Statistical Planning and Inference 143, 2101-2111.

o   Huang, X. (2013). Tests for random effects in linear mixed models using missing data. Statistica Sinica 23, 1043-1070.

o   Huang, X. (2011). Detecting random-effects model misspecification via coarsened dataComputational Statistics and Data Analysis 55, 703-714.

o   Huang, X. (2009). An improved test of latent-variable model misspecification in structural measurement error models for group testing data. Statistics in Medicine 28, 3316-3327.

o   Huang, X., Stefanski, L. A., and Davidian, M. (2009). Latent-model robustness in joint modeling for a primary endpoint and a longitudinal process. Biometrics 65, 719-727.

o   Huang, X. and Tebbs, J. M. (2009). On latent-variable model misspecification in structural measurement error models for binary response. Biometrics 65, 710-718.

o   Huang, X. (2009). Diagnosis of random-effect model misspecification in generalized linear mixed models for binary response. Biometrics 65, 361-368.

o   Huang, X., Stefanski, L. A., and Davidian, M. (2006). Latent-model robustness in structural measurement error models. Biometrika 93, 53-64.