Address

Department of Applied Math, National Dong Hwa University, Hualien 97401, Taiwan

Phone: (03) 863-3520;     Fax: (03) 863-3510

Email: chtsaoATmail.ndhu.edu.tw  Web: http://plato.am.ndhu.edu.tw/~chtsao/

Education

1990-1994 Ph.D., Statistics, Cornell University

Dissertation:  "Confidence Estimation and Conditional Inference for Fieller's Confidence Sets"
Chair: Prof. J. T. Gene Hwang.
1983-1987 B.S., Mathematics. National Tsing Hwa University, Taiwan

Professional Positions

Department of Applied Math, National Dong Hwa University

• Professor: 2005 to present
• Associate Professor: 1997 to 2005; Assistant Professor : 1995 to 1997

Institute of Math Statistics, National Chung Cheng University

• Visiting Associate Professor: 1994 to 1995

Publications

1. Tsao, C.A. (2014).  A Statistical Introduction to Ensemble Learning Methods. Journal of Chinese Statistical Association. 52(1): 115-132. (Econlit, CIS)
2. Tsao, C.A. (2011). Take it to the limit: statistical paradoxes. Prasanta S. Bandyopadhyay and Malcolm Forster (eds.), Philosophy of Statistics: Vol. 7 Handbook of the Philosophy of Science. Elsevier. 737-748.
3. Chen, W.D. and Tsao, C.A. (2010). Consistent of boosting under normal assumption. Taiwanese Journal of Mathematics 14:6, 2125-2136. (SCI).
4. Tsao, C.A. and Tseng, Y.-L. (2010). Power estimation for testing normal means. Statistics. 44:3,  247 - 259. (SCI).
5. Tsai, C.C. and Tsao, C.A. (2009). Post-data Estimation of Binomial Testing Power. Taiwanese Journal of Mathematics. 13:6(B), 1867-1883. (SCI)
6. Tsao, C.A. and Chang, Y.-C.I. (2007). A stochastic approximation view of boosting. Computational Statistics and Data Analysis 52, 325-334.(SCI)
7. Tsao, C.A. and Tseng, Y.-L. (2006). Confidence estimation for tolerance intervals. Annals of the Institute of Statistical Mathematics 58, 441--456.  (SCI).
8. Tsao, C.A. (2006). Assessing post-data weight of evidence. Journal of Statistical Planning and Inference 136, 4012-4025. (SCI)
9. Tsao, C.A. (2006). A note on Lindley’s paradox. Test 15, 125--139. (SCI)
10. Wu, S.-S. and Tsao, C.A. (2004). Boosting under bivariate normality. Journal of the Chinese Statistical Society. 42, 259—275. (Econlit, CIS=Current Index to Statistics)
11. Tsao, C.A. and Tseng, Y.-L. (2004). A statistical treatment of the problem of division. Metrika. 59, 289-303. (SCI)
12. Tsay, J.J. and Tsao, C.A. (2003). Statistical gambler’s ruin problem. Communication in Statistics: Theory and Methods. 32, 1337--1359. (SCI)
13. Tsao, C.A. (2001). Combining p-values: A single experiment evaluation. Calcutta Statistical Association Bulletin. 51, 61--71. (CIS)
14. Tsao, C.A. and Hwang, J.T. (1999). Generalized Bayes confidence estimators for Fieller’s confidence sets. Statistics Sinica 9, 795--810. (SCI)
15. Tsao, C.A. and Hwang, J.T.G. (1998). Improved confidence estimators for Fieller’s confidence sets. Canadian Journal of Statistics 26, 299--310. (SCI)
16. Tsao, C.A. (1998). Conditional coverage probability of confidence intervals of errors-in-variables and related models. Statistics and Probability Letters 40, 165--170. (SCI)
17. Tsao, C.A. (1994).  Confidence Estimation and Conditional Inference for Fieller's Sets. Ph.D. Thesis, Statistics Center, Cornell University, Ithaca, N.Y. USA.

Conferences and Workshops

1. Chang, H.-W. and Tsao, C.A. (2014). Meter classification of Midi audio. The 23rd Southern Taiwan Statistical Conference. June  27-28, Hualien.
2. Hsu, R.-H. and Tsao, C.A. (2014).  Finding keys for midi sources. The 23rd Southern Taiwan Statistical Conference. June  27-28, Hualien.
3. Hung, B.-Z. and Tsao, C.A. (2013).  A comparison of random average regression methods. The 22nd Southern Taiwan Statistical Conference. June  28-29, Kaohsiung.
4. Cheng, C.-J. and Tsao, C.A. (2013).  Midi Query-by-Humming: a Jigsaw matching approach. The 22nd Southern Taiwan Statistical Conference. June  28-29, Kaohsiung.
5. Tsao, C.A. (2012). Naivity can be good: a theoretical study of naïve regression. The 8th Cross-Strait Conference on Probability and Statistics. August 14-16. Harbin, China.
6. Tsao, C.A. (2012). Naivity can be good: a theoretical study of naïve regression. The 8th world Congress in Probability and Statistic. July 9--14. Istanbul, Turkey. 
7. Chen, L.-Y. and Tsao, C.A. (2012).  Artificial prediction markets for classification. The 21st Southern Taiwan Statistical Conference. June  29-30,  New Taipei City.
8. Peng, Y.-K. and Tsao, C.A. (2012).  Matching numbers from touch tone WAV sources. The 21st Southern Taiwan Statistical Conference. June  29-30,  New Taipei City.
9. Li, Y. and Tsao, C.A. (2011).  Matching chords for Midi songs. The 20th Southern Taiwan Statistical Conference. June 24-25,  Chiayi.
10. Liu, F.-Y. and Tsao, C.A. (2011).  Interpreting prediction market under a survey framework. The 20th Southern Taiwan Statistical Conference. June 24-25, National Chung-Cheng University, Chiayi.
11. Kuo, J.L. and Tsao, C.A. (2010).  Beauty as averageness? An FDA approach to multi-prototype classification. The 19th Southern Taiwan Statistical Conference and the 7th Cross-Strait Conference on Probability and Statistics. July 6-7, Tainan.
12. Shih, W.T. and Tsao, C.A. (2010).  Random base learner for boosting. The 19th Southern Taiwan Statistical Conference and the 7th Cross-Strait Conference on Probability and Statistics. July 6-7, Tainan.
13. Tseng, Y.-L. and Tsao, C.A. (2009). ROC for testing composite hypotheses. IMS-APRM (IMS Asia Pacific Rim Meetings) June 28 - July 1, 2009, Seoul, Korea.
14. Lin, J.P. and Tsao, C.A. (2009).  A note on false discovery rate. The 18th Southern Taiwan Statistical Conference. June 26-27, Kaohsiung.
15. Wang, T. and Tsao, C.A. (2009). A LDA variant for unsupervised learning. The 18th Southern Taiwan Statistical Conference. June 26-27, Kaohsiung.
16. Chen, W.D. and Tsao, C.A. (2009). AHCBoost: Boosting for multi-class classification. The 6th Cross-Strait Conference on Probability and Statistics. Jan 10-12, Nanjing, China.
17. Chen, W.D. and Tsao, C.A. (2008). AHCBoost: Boosting for multi-class classification. Joint Meeting of 4^th World Conference of the IASC and 6^th Conference of the Asian Regional Section of the IASC on computational statistics and data analysis. 5 - 8 December, Yokohama, Japan.
18. Chen, W.D. and Tsao, C.A. (2007). Consistent of boosting under normal assumption. 56th Session of the ISI, 22 - 29 Aug, Lisboa, Portugal.
19. Chen, W.D. and Tsao, C.A. (2007). A Sample perspective of Boosting convergence. 2007 Taipei International Statistical Symposium and ICSA International Conference.
20. Chen, W.D. and Tsao, C.A. (2006). A Bayesian estimation view of boosting. Compstat 2006, 17^th Conference of IASC-ERS. Aug. 28 – Sep 1 2006, Rome, Italy.
21. Lin, M.Y. and Tsao, C.A. (2006). Parameters Tuning in Heuristics for Classification. Southern Taiwan Statistical Conference. Chiayi.
22. Chen, Y.T. and Tsao, C.A. (2006). Google Page Ranks with partial incidence matrices. Southern Taiwan Statistical Conference. Chiayi.
23. Tsao,C.A.and Chang,Y.-C.I. (2005). A stochastic approximation view of  boosting.3rd IASC world conference on Computational Statistics and Data Analysis,Cyprus.
24. Tsao,C.A.and Chang,Y.-C.I. (2005). A stochastic approximation view of  boosting. Southern Taiwan Statistical Conference.Tainan.
25. Wu, S.-S. and Tsao, C.A. (2003). Boosting under bivariate normality. 2003 Southern Taiwan Statistical Conference. Kaohsiung.
26. Tsao, C.A. (2003). A note on Lindley’s paradox. Recent Advancement in Statistics Workshop. Tamkang University, Taipei.
27. Tsao, C.A. (2002). Assessing post-data weight of evidence. The 2002 Taipei International Statistical Symposium and Bernoulli Society EAPR Conference.
28. Tsao, C.A. and Tseng, Y.-L. (2001).  Predicting the correct prediction. International Chinese Statistical Society Conference. Academia Sinica, Taipei.
29. Tsao, C.A. and Tseng, Y.-L. (1999).  A statistical approach to the problem of division. The 52nd ISI conference. Helsinki, Finland.
30. Tsao, C.A. (1998).  Combining p-values: A single experiment approach. 3rd conference on Statistical Computing of the Asian Regional Section. Manila, Philippines.

1. Statistical Machine Learning, Multivariate Data Analysis
2. Applied Linear Models, Sampling Theory, Categorical Data Analysis
3. Mathematical Statistics, Advance Probability Theory, Statistical Decision Theory
4. Introduction to Probability, Statistics, Calculus, Linear Algebra for Engineers
   
5. Introduction to Statistics and Probability (for non-majors)