Wenge Guo

Papers & Technical Reports

2026

• Aldirawi, T., Li, Y., and Guo, W. (2026). Non-Monotonicity in Conformal Risk Control. arXiv preprint arXiv: 2604.01502. [Arxiv]

• Qiu, Z., Yu, L., and Guo, W. (2026). A Graphical Framework for Testing Hierarchically Structured Hypothesis Families. Statistics in Biopharmaceutical Research, in press. [PDF]

2025

• Xu, Y., Ying, M., Guo, W., and Wei, Z. (2025). Two-stage Risk Control with Application to Ranked Retrieval. In 34th Internationa Joint Conference on Artificial Intelligence, IJCAI 2025 (pp. 9104-9111). [PDF] [Arxiv]

• Luo, X., Li, L., Savenkov, O., Liu, W., Ni, X., Tang, W., and Guo, W. (2025). Multiple Comparisons Procedures for Analyses of Joint Primary Endpoints and Secondary Endpoints. Pharmaceutical Statistics, 24:e70010. [PDF]

2024

• Ying, M., Guo, W., Khamaru, K., and Hung, Y. (2024). Informativeness of Weighted Conformal Prediction. Working paper. [PDF] [Arxiv]

• Chakraborty, S., Tyagi, C., Qiao, H., and Guo, W. (2024). Distribution-free Conformal Prediction for Ordinal Classification. In COPA 2024 : 13th Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 230:120-139. [PDF] [Arxiv]

2023

• Tyagi, C. and Guo, W. (2023). Multi-label Classification under Uncertainty: A Tree-based Conformal Prediction Approach. In COPA 2023 : 12th Symposium on Conformal and Probabilistic Prediction with Applications, pp. 488-512, PMLR. [PDF][Arxiv]

• Xu, Y., Guo, W., Wei, Z. (2023). Conformal Risk Control for Ordinal Classification. In UAI 2023: The 39th Conference on Uncertainty in Artificial Intelligence, PMLR 216:2346-2355. [PDF] [Supp Info][Arxiv]

2022

• Ghosh, S., Guo, W., and Ghosh, S. (2022). A hierarchical testing procedure for three arm noninferiority trials.
  Computational Statistics & Data Analysis, 174, 107521. [PDF]

2020

• Zhu, Y. and Guo, W. (2020).
  Familywise error rate controlling procedures for discrete Data. Statistics in Biopharmaceutical Research, 12, 117-128. [PDF] [Supp Info] [Arxiv] [R Package]

• Guo, W. and Sarkar, S. (2020).
  Adaptive controls of FWER and FDR under block dependence.
  Journal of Statistical Planning and Inference, 208, 13-24. [PDF] [Arxiv]

• Dong, B., Wang, H., Monreale, A., Pedreschi, D., Gianotti, F. and Guo, W. (2020). Authenticated Outlier Mining for Outsourced Databases. IEEE Transactions on Dependable and Secure Computing, 17(2), 222 - 235. [PDF]

2019

• Grandhi, A., Guo, W. and Romano, J. P. (2019).
  Control of directional errors in fixed sequence multiple testing.
  Statistica Sinica, 29, 1047-1064. [PDF] [Arxiv] [R Package]

• Sarkar, S., Chen, A., He, L. and Guo, W. (2019). Group sequential BH and its adaptive versions controlling the FDR.
  Journal of Statistical Planning and Inference, 199, 219-235. [PDF]

2018

• Guo, W., Lynch, G. and Romano, J. P. (2018).
  A new approach for large scale multiple testing with application to FDR control for graphically structured hypotheses. Submitted for publication. [PDF] [Arxiv]

2017

• Guo, W. and Romano, J. P. (2017).
  Analysis of error control in large scale two-stage multiple hypothesis testing. Submitted for publication. [PDF] [Arxiv]

• Lynch, G., Guo, W., Sarkar, S. and Finner, H. (2017).
  The control of the false discovery rate in fixed sequence multiple testing. Electronic Journal of Statistics, 11, 4649-4673. [PDF] [Arxiv] [R Package]

2016

• Sarkar, S., Fu, Y. and Guo, W. (2016).
  Improving Holm's procedure using pairwise dependencies. Biometrika, 103, 237-243. [PDF] [Supp Info]

• Grandhi, A., Guo, W. and Peddada, S. (2016).
  A multiple testing procedure for multi-dimensional pairwise comparisons with application to gene expression studies. BMC Bioinformatics, 17:104. [PDF] [Supp Info]

• Qiu, Z., Guo, W. and Sarkar, S. (2016).
  Bonferroni-based gatekeeping procedures with retesting option. Submitted for publication. [PDF] [Arxiv]

• Lynch, G. and Guo, W. (2016).
  On procedures controlling the FDR for testing hierarchically ordered hypotheses. Submitted for publication. [PDF] [Arxiv]

2015

• Guo, W. and Romano, J. P. (2015).
  On stepwise control of directional errors under independence and some dependence. Journal of Statistical Planning and Inference, 163, 21-33. [PDF] [Arxiv]

• Qiu, Z., Guo, W. and Lynch, G. (2015).
  On generalized fixed sequence procedures for controlling the FWER. Statistics in Medicine, 34, 3968-3983. [PDF] [R Package]

2014

• Guo, W., Li He and Sarkar, S. (2014).
  Further results on controlling the false discovery proportion. Annals of Statistics, 42, 1070-1101. [PDF] [Supp Info]

• Guo, W. and Sarkar, S. (2014).
  Stepdown procedures controlling a generalized false discovery rate.
  In Statistical Science and Interdisciplinary Research: Statistical Paradigms, Recent Advances and Reconciliations, edited by A. SenGupta, T. Samanta, and A. Basu, Vol. 14, World Scientific. [PDF]

2013

• Sarkar, S., Chen, J. and Guo, W. (2013).
  Multiple testing in a two-stage adaptive design with combination tests controlling FDR. Journal of the American Statistical Association, 108, 1385-1401. [PDF] [Supp Info]

2012

• Guo, W., Yang, M., Xing, C. and Peddada, S. (2012).
  Analysis of high dimensional data using pre-defined set and subset information, with applications to genomic data. BMC Bioinformatics, 13:177. [PDF]

• Sarkar, S., Guo, W. and Finner, H. (2012).
  On adaptive procedures controlling the familywise error rate.
  Journal of Statistical Planning and Inference, 142, 65-78. [PDF]

• Clements, N., Sarkar, S. and Guo, W. (2012).
  Astronomical transient detection controlling the false discovery rate. In Statistical Challenges in Modern Astronomy, edited by Eric D. Feigelson and G. Joseph Babu, Lecture Notes in Statistics, Vol. 209, Part 4, Springer-Verlag, 383-396. [PDF]

• Liu, R., Wang, H., Monreale, A., Pedreschi, D., Giannotti, F. and Guo, W. (2012). AUDIO: An integrity auditing framework of outlier-mining-as-a-service systems. In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), September 24 - 28, 2012, Bristol, UK. [PDF]

2011

• Guo, W., Sarkar, S. and Peddada, S. (2011).
  Adaptive multiple testing procedures under positive dependence.
  In Recent Advances in Biostatistics: False Discovery Rates, Survival Analysis and Other Topics, edited by M. Bhattacharjee, S. Dhar, and S. Subramanian, Series in Biostatistics, Vol. 4, World Scientific, 27-41. [PDF]

2010

• Guo, W. and Rao, M. B. (2010).
  On stepwise control of the generalized familywise error rates.
  Electronic Journal of Statistics, 4, 472-485. [PDF] An earlier version appears here.

• Sarkar, S. and Guo, W. (2010).
  Procedures controlling generalized false discovery rate using bivariate distributions of the null p-values.
  Statistica Sinica, 20, 1227-1238. [PDF] [Supp Info]

• Guo, W., Sarkar, S. and Peddada, S. (2010).
  Controlling false discoveries in multidimensional directional decisions, with applications to gene expression data on ordered categories.
  Biometrics, 66(2), 485-492. [PDF] [Supp Info1] [Supp Info 2]

2009

• Guo, W. (2009).
  A note on adaptive Bonferroni and Holm's procedures under dependence.
  Biometrika, 96, 1012-1018. [PDF]

• Sarkar, S. and Guo, W. (2009).
  On a generalized false discovery rate.
  Annals of Statistics, 37, 1545-1565. [PDF]

2008

• Guo, W. (2008).
  Comment on: Control of the false discovery rate under dependence using the bootstrap and subsampling tests.
  Test, 17, 446-449. [PDF]

• Guo, W. and Peddada, S. (2008).
  Adaptive choice of the number of bootstrap samples in large scale multiple testing.
  Statistical Applications in Genetics and Molecular Biology, 7(1), Article 13. [PDF]

• Guo, W. and Rao, M. B. (2008).
  On optimality of the Benjamini-Hochberg procedure for the false discovery rate.
  Statistics and Probability Letters, 78, 2024-2030. [PDF]

• Guo, W. and Rao, M. B. (2008).
  On control of the false discovery rate under no assumption of dependency.
  Journal of Statistical Planning and Inference, 28, 3176-3188. [PDF]

2007 and earlier

• Guo, W. and Romano, J. (2007).
  A generalized Sidak-Holm procedure and control of generalized error rates under independence.
  Statistical Applications in Genetics and Molecular Biology, 6(1), Article 3. [PDF]

• Guo, W., Kasala, S., Rao, M. B., and Tucker, B. (2006).
  The hat problem and its variants.
  In Advances in Distribution Theory, Order Statistics, and Inference, edited by N. Balakrishnan, E. Castillo, and J. M. Sarabia, Birkhauser Publisher, 459-479. [PDF]