Research Highlight
                                                  My Research Highlight

Research Interests

My primary research interests lie in developing cutting-edge statistical methods and theory for solving problems arising from large-scale data with complex features, such as large diverse longitudinal, functional, heterogeneous or correlated data. These statistical methodologies have important applications in many areas, such as epidemiology, neuroimaging, financial economics, official statistics, transportation, plant science, environmental studies, and biomedical science.

       Neuroscience  COVID  Environmental Studies   CD4    Traffic   Corn Field   






  • Statistical Machine Learning for Complex & Large-scale Data
  • Non/semi-parametric Regression
  • Survey Sampling
  • Functional Data Analysis
  • Imaging Data Analysis
  • Spatiotemporal Data Analysis
  • High-dimensional Data Analysis
  • Financial Economics



Selected Publications

  • Wang, L., Wang, G.*, Li, X.*, Yu, S.*, Kim, M.*, Wang, Y.*, Gu, Z.* and Gao, L.  (2021). Modeling and forecasting COVID-19.  AMS: Notices Of The American Mathematical Society, 68, 585-595.   Read PDF 
  • Li, X.*, Wang, L. and Wang, H. (2021+). Sparse Learning and Structure Identification for Ultra-High-Dimensional Image-on-Scalar Regression. Journal of the American Statistical Association, Theory and Methods. In press.
  • Yu, S.*, Wang, G.*, Wang, L. and Yang, L. (2021). Multivariate spline estimation and inference for image-on-scalar regression. Statistica Sinica, 31, 1463-1487. [An early version was selected as one of three runners-up of the 2019 ASA Statistics in Imaging Section Student Paper Competition]     Read PDF
  • Wang, Y., Wang, G., Wang, L. and Ogden, T. (2020). Simultaneous confidence corridors for mean functions in functional data analysis of imaging data. Biometrics, 76, 427--437.  Read PDF
  • Yu, S., Wang, G., Wang, L., Liu, C. and Yang, L. (2020). Estimation and inference for generalized geoadditive models. Journal of the American Statistical Association, Theory and Methods, 115, 761-774.  Read PDF
  • Datta, G. S., Delaigle, A., Hall, P. and Wang, L. (2018). Semi-parametric prediction intervals in small areas when auxiliary data are measured with error. Statistica Sinica, 28, 2309-2335.  Read PDF
  • Mu, J., Wang, G. and Wang, L. (2018). Estimation and inference in spatially varying coefficient models. Environmetrics, 29:e2485. Read PDF (Environmetrics's top 20 most downloaded recent papers!)
  • Song, X. and Wang, L. (2017). Partially time-varying coefficient proportional hazards models with error prone time-dependent covariates: an application to the AIDS Clinical Trials Group 175 Data. Annals of Applied Statistics, 11, 274-296.  Read PDF
  • Wang, L., Xue, L., Qu, A. and Liang, H. (2014). Estimation and model selection in generalized additive partial linear models for correlated data with diverging number of covariates. Annals of Statistics, 42, 592-624. Read PDF
  • Lai, M. J. and Wang, L. (2013). Bivariate penalized splines for regression. Statistica Sinica, 23, 1399-1417. Read PDF
  • Wang, L., Liu X., Liang, H. and Carroll, R. J. (2011). Estimation and variable selection for generalized additive partial linear models. Annals of Statistics, 39, 1827-1851.  Read PDF
  • Wang, L. and Yang, L. (2009). Spline estimation of single-index models. Statistica Sinica, 19, 765-783.  Read PDF
  • Wang, L. and Yang, L. (2007). Spline-backfitted kernel smoothing of nonlinear additive autoregression model. Annals of Statistics, 35, 2474-2503.  Read PDF


  • NSF DMS & SES 
  • SEC               
  • Iowa State University
  • University of Georgia