Home       Research       Teaching       Software



                                    Papers and Technical Reports


           Wenguang Sun

*indicates our PhD students




1.     Fu, L.*, James, G., and Sun, W. (2017).

Nonparametric empirical Bayes Tweedie estimator for normal means with heteroscedastic errors.

Technical Report.


2.     Banerjee, T.*, Mukerjee, G. and Sun, W. (2017).

Adaptive sparse estimation with side information.

Technical Report.


3.     Wang, W.*, and Sun, W. (2017).

Multistage adaptive testing for sparse recovery.

Technical Report.


4.     Xia, Y., Cai, T. and Sun, W. (2017).

GAP: a general framework for information pooling in two-sample sparse inference.

Technical Report.




5.     Basu, P.*, Cai, T., Das, K., and Sun, W. (2016).

Weighted false discovery rate control in large-scale multiple testing.

Journal of the American Statistical Association, to appear.


6.     Cai, T., Sun, W., and Wang, W.* (updated version in September 2017).

CARS: covariate assisted ranking and screening for large-scale two-sample inference.

Technical Report.


7.     Feng, T.*, Basu, P.*, Sun, W., and Mack, W. and Ku, T. (2016).

Control of false discovery rate and optimal design for high-throughput screening.

Technical Report.


8.     Cai, T., and Sun, W. (2017).

Large-Scale Global and Simultaneous Inference: Estimation and Testing in Very High Dimensions.

Annual Review of Economics, Vol. 9, 411-439.




9.     Cai, T., and Sun, W. (2017).

Optimal discovery and screening of sparse signals with applications to multistage high-throughput studies.

Journal of the Royal Statistical Society, Series B, 79, 197-223.


10.  Sun, W., Reich, B., Cai, T., Guindani, M., and Schwartzman, A. (2015).

False discovery control in large-scale spatial multiple testing.

Journal of the Royal Statistical Society, Series B, 77, 59-83.


11.  Sun, W. and Wei, Z. (2015).

Hierarchical recognition of sparse patterns in large-scale simultaneous inference.

Biometrika, 102, 267-280.




12.  Cao, H., Sun, W., Kosorok, M. (2013).

The optimal power puzzle: scrutiny of the monotone likelihood ratio assumption in multiple testing.

Biometrika, 100, 495-502.




13.  Sun, W., and McLain, A. (2012).

Multiple testing of composite null hypotheses in heteroscedastic models.

Journal of the American Statistical Association, 498, 673-687.


14.  James, G., Sun, W., and Qiao, X. (2012).

Discussion of ˇ°Clustering random curves under dependenceˇ± by Serban and Jiang.

Technometrics, 54, 123-126.




15.  Sun, W., and Wei, Z. (2011).

Multiple testing for pattern identification, with applications to time-course microarray experiments.

Journal of the American Statistical Association, 106, 73-88.




16.  Sun, W., Joffe, M., Chen, J., and Brunelli, S. (2010).

Design and analysis of multiple events case-control studies.

Biometrics, 66, 1220-1229.


17.  Wang, W., Wei, Z., and Sun, W. (2010).

Simultaneous set-wise testing under dependence, with applications to genome-wide association studies.

Statistics and Its Interface, 3, 501-511.


18.  Cai, T., and Sun, W. (2010).

A compound decision-theoretic approach to large-scale multiple testing.

Book chapter for Analysis of High Dimensional Data, edited by Cai, T. and Shen, X..




19.  Cai, T., and Sun, W. (2009).

Simultaneous testing of grouped hypotheses: finding needles in multiple haystacks.

Journal of the American Statistical Association, 104, 1467-1481.     


20.  Wei, Z., Sun, W., Wang, K., and Hakonarson, H. (2009).

Multiple testing in genome-wide  association studies via hidden Markov models.

Bioinformatics, 25, 2802-2808.


21.  Sun, W., and Cai, T. (2009).

Large-scale multiple testing under dependence. 

Journal of the Royal Statistical Society, Series B, 71, 393-424.


22.  Shults, J., Sun, W., Tu, X., Kim, H., Amsterdam, J., and Ten Have, T. (2009).

On choosing the working correlation structure in the GEE analysis of longitudinal binary data. 

Statistics in Medicine, 28, 2338-2355.


23.  Sun, W., Shults, J., and Leonard, M. (2009).

A note on the use of unbiased estimating equations to estimate correlation in GEE analysis of longitudinal trials.

Biometrical Journal, 51, 5-18.


2008 & Earlier


24.  Sun, W., Joffe, M., Localio, R., and Norman, S. (2008).

Adjusting for Exposure Opportunity in Observational Studies of Cancer Screening.

Technical report.


25.  Sun, W., and Cai, T. (2007).

Oracle and adaptive compound decision rules for false discovery rate control. 

Journal of the American Statistical Association, 102, 901-912.


26.  Mansson, R., Hennessy, S., Sun, W., and Joffe, M. (2007).

On the estimation and use of propensity scores in case-control and case-cohort studies.

American Journal of Epidemiology, 166, 332-339.


27.  Tu, X., Zhang, J., Kowalski J., Shults J., Feng C., Sun, W., Tan W. (2007).

Power analyses for longitudinal study designs with missing data.

Statistics in Medicine, 26, 2958-2981.


28.  Shults, J., Sun, W., Tu, X., and Amsterdam, J. (2006).

On the violation of bounds for the correlation in GEE analyses of binary data  from longitudinal trials.

BEPRESS, University of Pennsylvania, Biostatistics Paper 8.