contact information

Weixin Yao (CV)

Professor and Vice Chair of Statistics
1422 Olmsted Hall
University of California
Riverside, CA 92521

Phone: (951) 827-6007

Research Interest

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


Selected Referred Publications(Full List)

  • 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]
  • Xiang, S. and Yao, W. (2018). Semiparametric Mixtures of Nonparametric Regressions. Annals of the Institute of Statistical Mathematics, 70, 131-154. [pdf]
  • Hu, H., Yao, W., and Wu, Y. (2017). The robust EM-type algorithms for log-concave mixtures of regression models. Computational Statistics and Data Analysis, 111, 14-26. [pdf]
  • Chen, Y. and Yao, W. (2017). Unified Inference for Sparse and Dense Longitudinal Data in Time-Varying Coe.cient Models. Scandinavian Journal of Statistics, 44, 268-284. [pdf]
  • Yu, C. and Yao, W. (2017). Robust Linear Regression: A Review and Comparison. Communications in Statistics-Simulation and Computation, 46, 6261-6282. [pdf]
  • Xiang, S., Yao, W. and Seo, B. (2016). Semiparametric Mixture: Continuous Scale Mixture Approach. Computational Statistics and Data Analysis, 103, 413-425. [pdf]
  • Hu, H., Wu, Y., and Yao, W. (2016). Maximum Likelihood estimation of mixture of log-concave densities. Computational Statistics and Data Analysis, 101, 137-147. [pdf]
  • Wang, S., Huang, M., Wu, X., and Yao, W. (2016). Mixture of Functional Linear Models and Its Application to CO2-GDP Functional Data. Computational Statistics and Data Analysis, 97, 1-15. [pdf]
  • Wu, Q. and Yao, W. (2016). Mixtures of quantile regressions. Computational Statistics and Data Analysis, 93, 162-176. [pdf]
  • Kurum, E., Li, R., Shiffman, S., and Yao, W. (2016). Time-Varying Coefficient Models for Joint Modeling Binary and Continuous Outcomes in Longitudinal Data. Statistica Sinica, 26, 979-1000. [pdf]
  • Yu, C., Chen, K., and Yao, W. (2015). Outlier Detection and Robust Mixture Modeling Using Nonconvex Penalized Likelihood. Journal of Statistical Planning and Inference, 164, 27-38. [pdf]
  • Ma, Y. and Yao, W. (2015). Flexible Estimation of A Semiparametric Two-component Mixture Model With One Parametric Component. Electronic Journal of Statistics, 9, 444-474. [pdf]
  • Chen, Y., Wang, Q., and Yao, W. (2015). Adaptive Estimation for Varying Coefficient Models. Journal of Multivariate Analysis, 137, 17-31.[pdf]
  • Yao, W. (2015). Label Switching and Its Simple Solutions for Frequentist Mixture Models. Journal of Statistical Computation and Simulation, 85, 1000-1012. [pdf]
  • Wang, S., Yao, W.., and Huang, M. (2014). A Note On the Identifiability of Nonparametric and Semiparametric Mixtures of GLMs. Statistics and Probability Letters, 93, 41-45. [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]
  • Song, W., Yao, W., and Xing Y. (2014). Robust mixture regression model fitting by laplace distribution. Computational Statistics and Data Analysis, 71, 128-137. [pdf]
  • Yao, W., Wei, Y., and Yu, C. (2014). Robust Mixture Regression Using T-Distribution and Profile Likelihood. Computational Statistics and Data Analysis, 71, 116-127. [pdf]
  • Yao, W. and Wang, Q. (2013). Robust variable selection through MAVE. Computational Statistics and Data Analysis, 63, 42-49. [pdf]
  • Yao, W. (2013). A Note On EM Algorithm For Mixture Models. Statistics and Probability Letters, 83, 519-526. [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]
  • Yao, W. and Zhao, Z. (2013). Kernel Density Based Linear Regression Estimates. Communications in Statistics-Theory and Methods, 42, 4499-4512. [pdf]
  • Lindsay, B. G. and Yao, W. (2012). Fisher Information Matrix: A Tool for Dimension Reduction, Projection Pursuit, Independent Components Analysis, and More. The Canadian Journal of Statistics, 40, 712-730. [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]
  • 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. (2012). Model Based Labeling for Mixture Models. Statistics and Computing, 22, 337-347. [pdf]
  • Bai, X., Yao, W., and Boyer, J. E. (2012). Robust Mixture of Regression. Computational Statistics and Data Analysis, 56, 2347-2359. [pdf]
  • Wang, Q. and Yao, W. (2012). An Adaptive Estimation of MAVE. Journal of Multivariate Data Analysis, 104, 88-100.[pdf]
  • Cao, J. and Yao, W. (2012). Semiparametric Mixture of Binomial Regression with a Degenerate Component. Statistica Sinica, 22, 27-46. [pdf]
  • Yao, W. (2010). A Profile Likelihood Method for Normal Mixture with Unequal Variance. Journal of Statistical Planning and Inference, 140, 2089-2098. [pdf] , Matlab code.
  • 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.