research interest

  • Mixture models
  • Modal regression
  • Robust data analysis
  • High dimensional modeling
  • Nonparametric and semiparametric modeling
  • Variable selection
  • Dimension reduction
  • Clustering

    • links

     

    Weixin Yao, Ph.D. (CV)


    Professor of Statistics
    1422 Olmsted Hall
    University of California
    Riverside, CA 92521
    Phone: (951)827-6007
    Email: weixin.yao@ucr.edu

    Dr. Yao received his Ph.D. in 2007 from the Department of Statistics, The Pennsylvania State University. He is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute.

     

    Research Interests

     
      Mixture models; Modal regression; Nonparametric and semiparametric modeling; Robust data analysis; High dimensional modeling; Variable selection; Dimension reduction.

     

    Editorial Services

     

     

    Selected Referred Publications(Full List)

     
    • Yao, W. and Xiang, S. (2024). Mixture Models: Parametric, Semiparametric, and New Directions. Chapman & Hall/CRC, ISBN 9780367481827.
    • Zeng, Z. and Yao, W. (2025). Pursuing homogeneity and sparsity in simultaneous quantile regression. Journal of Computational and Graphical Statistics.
    • Ullah, A., Wang, T., and Yao, W. (2023). Semiparametric Partially Linear Varying Coefficient Modal Regression. Journal of Econometrics, 235, 1001-1026.
    • Ullah, A., Wang, T., and Yao, W. (2022). Nonlinear Modal Regression for Dependent Data with Application for Predicting COVID-19. Journal of the Royal Statistical Society, Ser A., 185, 1424-1453. [pdf]
    • Li, Y., Yu, C., Zhao, Y., Yao, W., Aseltine, R., and Chen, K. (2022). Pursuing Sources of Heterogeneity in Modeling Clustered Population. Biometrics, 78, 716-729. [pdf]
    • Yao, W., Nandy, D., Lindsay, B., and Chiaromonte, F. (2019). Covariate Information Matrix for Sufficient Dimension Reduction. Journal of American Statistical Association, 114, 1752-1764. [pdf]
    • Xiang, S., Yao, W., and Yang, G. (2019). An Overview of Semiparametric Extensions of Finite Mixture Models. Statistical Science, 34, 391-404. [pdf]
    • Yao, W. and Li, L. (2014). A New Regression Model: Modal Linear Regression. Scandinavian Journal of Statistics, 41, 656-671. [pdf]
    • Huang, M., Li, R., and Huang, H., and Yao, W. (2014). Estimating Mixture of Gaussian Processes by Kernel Smoothing. Journal of Business and Economics Statistics, 32, 259-270. [pdf]
    • Yao, W. and Li, R. (2013). New Local Estimation Procedure for Nonparametric Regression Function of Longitudinal Data. Journal of the Royal Statistical Society, Ser B., 75, 123-138. [pdf]
    • Huang, M. and Yao, W. (2012). Mixture of Regression Models with Varying Mixing Proportions: A Semiparametric Approach. Journal of American Statistical Association, 107, 711-724. [pdf]
    • Yao, W., Lindsay, B. G., and Li, R. (2012). Local modal regression. Journal of Nonparametric Statistics, 24, 647-663. The winner of The Journal of Nonparametric Statistics Best Paper Award in 2015. [pdf]
    • Yao, W. and Lindsay, B. G. (2009). Bayesian Mixture Labeling by Highest Posterior Density. Journal of American Statistical Association, 104, 758-767. [pdf], Matlab code.