Software, Code and Dataset

• Kim, M., Wang, L. and Wang, H. (2025). Estimation and Inference of Quantile Spatially Varying Coefficient Models over Complicated Domains, Journal of the American Statistical Association, https://doi.org/10.1080/01621459.2025.2480867. Code and Data

• Peng, X. and Wang, H. (2023). Inference for joint quantile and expected shortfall regression. Stat, 12(1), e619. https://doi.org/10.1002/sta4.619. Code and Examples

• Peng, X. and Wang, H. (2022). A Generalized Quantile Tree Method for Subgroup Identification, Journal of Computational and Graphical Statistics, 31:3, 824-834, DOI: 10.1080/10618600.2022.2032723. Code and Examples

• Yingying Zhang, Huixia Judy Wang & Zhongyi Zhu (2021) Single-index Thresholding in Quantile Regression, Journal of the American Statistical Association, DOI: 10.1080/01621459.2021.1915319. R Package SITQR & Documentation

• Zhang, Y., Wang, H. and Zhu, Z. (2019). Quantile-regression-based clustering for panel data, Journal of Econometrics, DOI: 10.1016/j.jeconom.2019.04.005. R code for the simulation study

• Agarwala, G., Sun, Y. and Wang, H. (2021). Copula-based multiple indicator kriging for non-Gaussian random fields,  Spatial Statistics, 00524, https://doi.org/10.1016/j.spasta.2021.100524. R code (CMIK) and links to the datasets used in the application.

• Li, F., Tang, Y. and Wang, H. (2019). Copula-based semiparametric analysis for time series data with detection limits, Canadian Journal of Statistics, DOI:10.1002/cjs.11503. R Package CopCTS

• Tang, Y., Wang, H., Sun, Y. and Hering, A. (2019). Copula-based semiparametric models for spatio-temporal data, Biometrics, DOI:10.1111/biom.13066. R Package COST  & Documentation

• Gao, Z., Tang, Y., Wang, H., Wu, G. and Lin, J. (2019). Automatic shape-constrained nonparametric regression. Technical Report.  R Package SCR

• Wang, H., McKeague, I. and Qian, M. (2018). Testing for marginal linear effects in quantile regression, Journal of the Royal Statistical Society: Series B, 80, 433-452.   R package QMET    Help and Example File       HIV-EFV data

• R Package AdjBQR (Adjusted Bayesian Quantile Regression Inference)

Reference:  Yang, Y., Wang, H. and He, X. (2016). Posterior inference in Bayesian quantile regression with asymmetric Laplace likelihood (Discussion Paper). International Statistical Review, 84, 327-344.

• R package EXRQ (Extreme Regression of Quantiles)

• Jiang, L., Bondell, H. and Wang, H. (2014). Interquantile shrinkage and variable selection in quantile regression. Computational Statistics and Data Analysis, 69, 208-219. R functions

• Wang, H. and Li, D. (2013). Estimation of conditional high quantiles through power transformation. Journal of the American Statistical Association, 108, 1062-1074.   R function     Example

• Bernhardt, P., Wang, H., and Zhang, D. (2014). Flexible modeling of survival data with covariates subject to detection limits. Computational Statistics and  Data Analysis. 69, 81-91. Simulation code   Application code   Simulation data

• Bernhardt, P., Wang, H., and Zhang, D. (2013). Statistical methods for generalized linear models with covariates subject to detection limits.  Statistics in Biosciences, DOI 10.1007/s12561-013-9099-4. Simulation code   Application code  Simulation data

• Jiang, L., Wang, H. J., and Bondell, H. D. (2013). Interquantile shrinkage in regression models. Journal of Computational and Graphical Statistics, 22, 970-986. R code    Help and Example file

• Wang, H., Zhou, J., and Li, Y. (2013). Variable selection for censored quantile regression, Statistica
  Sinica, 23, 145-167.  R functions and a simulation study

• Wang, H. and Feng, X. (2012). Multiple imputation for M-regression with censored covariates, Journal of American Statistical Association, 107, 194-204.  R code    Example

• Wang, H., Stefanski, L., and Zhu, Z. (2012). Corrected-loss estimation for quantile regression with covariate measurement error. Biometrika, 99, 405-421. R code    Example

• Sun, Y., Wang, H., and Gilbert, P. B. (2011). Quantile regression for competing risks data with missing cause of failure, Statistica Sinica, 22, 703-728. R functions and a simulation study

• Pang, L., Lu, W., and Wang, H. (2012). Variance Estimation in Censored Quantile Regression via Induced Smoothing, Computational Statistics and Data Analysis, 56, 785-796. R functions and a simulation study

• Tang, Y., Wang, H., Zhu, Z. and Song, X. (2011). A unified variable selection approach for varying coefficient models. Statistica Sinica, 22, 601-628. R functions and example for LS     R functions and example for RQ

• Wang, H. and Hu, J. (2010). Identification of differential aberrations in multiple-sample array CGH studies. Biometrics, 67, 353-362. R functions 

• Wang, H., and Zhu, Z. (2010). Empirical likelihood for marginal regression models with longitudinal data. Journal of Statistical Planning and Inference. 141, 1603-1615.

R functions used in the Simulation study

R functions used for the analysis of an ophthalmology data

• Wang, H., Zhu, Z., and Zhou, J. (2009). Quantile regression in partially linear varying coefficient models. Annals of Statistics, 37, 3841-3866. R functions  An example

• Bondell, H. D., Reich, B. J., and Wang, H. (2010). Non-crossing quantile regression curve estimation. Biometrika. 97, 825-838.  R code  Help and example

• Reich, B. J., Bondell, H. D., and Wang, H. (2010). Flexible Bayesian quantile regression for independent and clustered data. Biostatistics, 11, 337-352. R code for independent and clustered data using the conditional and marginal model.

• Wang, H., and Wang, L. (2009). Locally weighted censored quantile regression. Journal of American Statistical Association, 104, 1117-1128. R code

• Wang, H. and Fygenson, M. (2009). Inference for censored quantile regression models in longitudinal studies. Annals of Statistics, 37, 756-781. R code

• Wang, H. (2009). Inference on quantile regression for heteroscedastic mixed models. Statistica Sinica, 19, 1247-1261. R code for a simulated data set mimicking the swallow study

• Wang, H. and He, X. (2008). An enhanced quantile approach for assessing differential gene expressions.  Biometrics, 449-457. R code    Mice data

• Wang, H. and He, X. (2007). Detecting differential expressions in GeneChip microarray studies: a quantile approach. Journal of American Statistical Association, 102, 104-112.  

R code: Quantile rank score test for models with a random intercept effect (clustered data). The R function is for more general models with a random intercept effect, and it can also be used for hypothesis testing for clustered data.