links

Software and Dataset

Sun, Y., Wang, H., and Gilbert, P. B. (2011). Quantile regression for competing risks data with missing cause of failure, Statistica Sinica, in press.

R functions and a simulation study

Pang, L., Lu, W., and Wang, H. (2010). Variance Estimation in Censored Quantile Regression via Induced Smoothing, Computational Statistics and Data Analysis. In press.

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. In press.

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. In press.

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 and help

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

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 mimicing 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.

The research of Huixia Judy Wang was supported by NSF awards DMS-07-06963, DMS-10-07420, and a NSF US-China Collaboration in Mathematical Research award.