Part I: Presentations
生科院卜文俊院长课题组
Part II: Papers
SPC and Sequential Problems:
1. Jeske, D. R., De Oca, V. M., Bischoff, W., & Marvasti, M. (2009). Cusum techniques for
timeslot sequences with applications to network surveillance.Computational Statistics &
Data Analysis, 53(12), 4332-4344.
2. Chen, Y., Birch, J. B., & Woodall, W. H. (2015). Cluster-Based Profile Analysis in Phase
I. Journal of Quality Technology, 47(1), 14.
3. Ambartsoumian, T., & Jeske, D. R. (2015). Nonparametric CUSUM Control Charts and
Their Use in Two-Stage SPC Applications. Journal of Quality Technology, 47(3).
4. Woodall, W.H., Zhao, M., Paynabar, K., Sparks,R., & Wilson, J.D.,(2015) An Overview and
Perspective on Social Network Monitoring
Change-Point and Outlier Detection
5. Aston, J. A. and Kirch, C. Change points in high dimensional settings, manuscript
6. Cao, H., & Wu, W. B. (2015). Changepoint estimation: another look at multiple testing
problems. Biometrika, asv031.
7. Jirak, M. (2015). Uniform change point tests in high dimension. arXiv preprint
8. Florian Pein, Hannes Sieling, & Axel Munk.(2015) Heterogeneuous Change Point
Inference.
Manuscript
9. Kirch, C., Muhsal, B., & Ombao, H. (2014). Detection of Changes in Multivariate Time
Series With Application to EEG data. Journal of the American Statistical Association,
(just-accepted), 00-00.
Big Data Analysis
10. Zhao, T., Cheng, G. and Liu, H. A Partially Linear Framework for Massive Heterogeneous
Data, Manuscript submitted to The Annals of Statistics.
11. Cox, D. R. (2015). Big data and precision. Biometrika, 102(3), 712-716.
12. Chen, X. and Xie, M. (2014), A Split-and-Conquer Approach for Analysis of.
Extraordinarily Large Data, Statistica Sinica, 24, 1655-1684.
13. Meinshausen, N., & Bühlmann, P. (2015). Maximin effects in inhomogeneous large-scale
data. The Annals of Statistics, 43(4), 1801-1830.
14. Fan, J., Han, F., & Liu, H. (2014). Challenges of big data analysis. National science
review, 1(2), 293-314.
Categorical Data Analysis
15. Li, Q., Maasoumi, E., & Racine, J. S. (2009). A nonparametric test for equality of
distributions with mixed categorical and continuous data. Journal of Econometrics, 148(2),
186-200.
16. Crane, H. (2014). Clustering from categorical data sequences. Journal of the American
Statistical Association, (just-accepted), 00-00.
17. Huang, D., Li, R., & Wang, H. (2014). Feature Screening for Ultrahigh Dimensional
Categorical Data With Applications. Journal of Business & Economic Statistics, 32(2),
237-244.
High-Dimensional Inference
18. Fan, Y., Kong, Y., Li, D., & Zheng, Z. (2015). Innovated interaction screening for
high-dimensional nonlinear classification. The Annals of Statistics, 43(3), 1243-1272.
19. Fan, J., Liao, Y., & Wang, W. (2014). Projected principal component analysis in factor
models. Available at SSRN 2450770.
20. Xia, Y., Cai, T., & Cai, T. T. (2015). Testing differential networks with applications to the
detection of gene-gene interactions. Biometrika, asu074.
21. Barber, R. F., & Candes, E. (2014). Controlling the false discovery rate via knockoffs. arXiv
preprint arXiv:1404.5609.
22. Pan, R., Wang, H., & Li, R. (2015). Ultrahigh dimensional multi-class linear discriminant
analysis by pairwise sure independence screening. Journal of the American Statistical
Association, (just-accepted)
23. Sgouropoulos, N., Yao, Q., & Yastremiz, C. (2014). Matching a distribution by matching
quantiles estimation. Journal of the American Statistical Association, (just-accepted)
24. Hao, N., Dong, B., & Fan, J. (2014). Sparsifying the Fisher linear discriminant by
rotation. Journal of the Royal Statistical Society: Series B (Statistical Methodology).