Henghsiu Tsai€

 

  蔡恆修

 Henghsiu Tsai

 

 Institute of Statistical Science

 Academia Sinica

 Taipei 11529, Taiwan, R.O.C.

 Phone:  886-2-27835611 Ext. 215

 Fax:       886-2-27831523

 E-mail:   htsai@stat.sinica.edu.tw



 

Research Interests

l  Mathematical Finance

l  Multivariate Data Analysis

l  Psychometrics

l  Sampling-based Inference

l  Stochastic Differential Equation Modeling

l  Stochastic Processes

l  Time Series Analysis

 

Education

Ph.D.

Statistics

The University of Iowa, Iowa City, Iowa, USA

 

 

 

 

 

 

 

Professional Experience

 

May 2013  -  present         Research Fellow                     Institute of Statistical Science, Academia Sinica

 

 

Jan. 2006  -  May 2013    Associate Research Fellow      Institute of Statistical Science, Academia Sinica

 

 

Aug. 2001  -  Jan. 2006    Assistant Research Fellow       Institute of Statistical Science, Academia Sinica

 

 

 

Aug. 1999  -  Jan. 2001    Assistant Professor                   Department of Statistics, Tunghai University

 

 

 

Publications

 Journal Articles 

  1. Tsai, H. and Nikitin, A.V. (2024). Threshold Models for Lévy Processes and Approximate Maximum Likelihood Estimation. Cybernetics and Systems Analysis, 60, 261-267.
  2. Rachinger, H., Lin, E.M.H., and Tsai, H. A bootstrap test for threshold effects in a diffusion process. Computational Statistics, in press.
  3. Das, M.K., Tsai, H. Kyriakou, I., and Fusai, G. (2022). On mtrix eponential dfferentiation with aplication to wighted sm dstributions. Operation Research, 70 (4), 1984-1995.
  4. Ho, H.-C., Chen, H.-Y., and Tsai, H. (2021). Non-parametric estimation of conditional tail expectation for long-horizon returns. Statistica Sinica, 31, 547-569.
  5. Yu, T.-H., Tsai, H., and Rachinger, H. (2020). Approximate maximum likelihood estimation of a threshold diffusion process, Computational Statistics and Data Analysis, 106823.
  6. Tsai, H., Rachinger, H., and Chan, K.S. (2018). Inference of bivariate long-memory aggregate time series, Statistica Sinica, 28, 399-421.
  7. Tsai, H., Tsay, R.S., Lin, E.M.H., and Cheng, C.-W. (2016). Doubly constrained factor models with applications, Statistica Sinica, 26, 1453-1478. 
  8. Ho, H.-C., Chen, H.-Y., and Tsai, H. (2016). Value at risk for integrated returns and its applications to equity portfolios, Statistica Sinica, 26, 1631-1648.
  9. Tsai, H., Rachinger, H., and Lin, E.M.H. (2015). Inference of seasonal long-memory time series with measurement error. Scandinavian Journal of Statistics, 42, 137-154.
  10. Cheng, T. L. and Tsai, H. (2014). On the ruin time for risk reserve processes when the claims have infinite expectation. Journal of the Chinese Statistical Association, 52, 435-448.
  11. Hassler, U. and Tsai, H. (2013). Asymptotic behavior of temporal aggregates in the frequency domain. Journal of Time Series Econometrics, 5, 47-60.
  12. Chan, K.S. and Tsai, H. (2012). Inference of seasonal long-memory aggregate time series. Bernoulli, 18, 1448-1464.
  13. Tsai, H., Chan, K.S., and Fayard, P. (2011). Testing for measurement errors with discrete-time data sampled from a CARMA model. Statistics and Its Interface, 4, 235-242.
  14. Tsai, H. and Tsay, R.S. (2010). Constrained factor models. Journal of the American Statistical Association, 105, 1593-1605.
  15. Hsu, N.-J. and Tsai, H. (2009). Semiparametric estimation for seasonal long-memory time series using generalized exponential models. Journal of Statistical Planning and Inference, 139, 1992-2009.
  16. Tsai, H. and Chan, K.S. (2009). A note on the non-negativity of continuous-time ARMA and GARCH processes.Statistics and Computing, 19, 149-153.
  17. Tsai, H. (2009). On continuous-time autoregressive fractionally integrated moving average processes. Bernoulli, 15,178-194.
  18. Tsai, H. and Chan, K.S. (2008). A note on inequality constraints in the GARCH Model. Econometric Theory , 24, 823-828.
  19. Tsai, H. and Chan, K.S. (2007). A note on non-negative ARMA processes. Journal of Time Series Analysis, 28, 350-360.
  20. Tsai, H. (2006). Quasi-maximum likelihood estimation of long-memory limiting aggregate processes. Statistica Sinica, 16, 213-226.
  21. Tsai, H. and Chan, K.S. (2005). Maximum likelihood estimation of linear continuous time long memory processes with discrete time data. Journal of the Royal Statistical Society, Series B, 67, 703-716.
  22. Tsai, H. and Chan, K.S. (2005). A note on non-negative continuous time processes. Journal of the Royal Statistical Society, Series B , 67, 589-597.
  23. Tsai, H. and Chan, K.S. (2005). Temporal aggregation of stationary and nonstationary discrete-time processes. Journal of Time Series Analysis, 26, 613-624.
  24. Tsai, H. and Chan, K.S. (2005). Temporal aggregation of stationary and non-stationary continuous-time processes. Scandinavian Journal of Statistics, 32, 583-597.
  25. Tsai, H. and Chan, K.S. (2005). Quasi-maximum likelihood estimation for a class of continuous-time long-memory processes. Journal of Time Series Analysis, 26, 691-713.
  26. Tsai, H. and Chan, K.S. (2003). A note on parameter differentiation of matrix exponentials, with applications to continuous-time modeling. Bernoulli, 9, 895-919.
  27. Tsai, H. and Chan, K.S. (2002). A note on testing for nonlinearity with partially observed time series. Biometrika, 89, 245-50.
  28. Tsai, H. and Chan, K.S. (2000). Testing for nonlinearity with partially observed time series. Biometrika, 87, 805-821.
  29. Tsai, H. and Chan, K.S. (2000). A note on the covariance structure of a continuous-time ARMA process. Statistica Sinica, 10, 989-998.

 

 Book Chapters / Technical Reports / Unpublished Manuscripts

1. Tsai, H., Ho, H.-C., and  Chen, H.-Y. (2020). Non-parametric inference on risk measures for integrated returns. In Handbook of Financial Economics, Mathematics, Statistics, and Machine Learning, 2020, ed. By C.-F. Lee and  J. C. Lee, World Scientific, Singapore, 2485-2497.

         

2. Su, Y.-H. and Tsai, H. (2019). Detection of differential item functioning via the credible intervals and odds ratios methods. In Quantitative Psychology – The 83th Annual Meeting of the Psychometric Society, Zurich, Switzerland, 2017, ed. By M. Wiberg, S. Culpepper, R. Janssen, J. González, & D. Molenaar, Springer International Publishing,  Switzerland, 319-330.

 

3. Su, Y.-H., Chang, J., and Tsai, H. (2018). Using credible intervals to detect differential item functioning in IRT models. In Quantitative Psychology – The 82th Annual Meeting of the Psychometric Society, Zurich, Switzerland, 2017, ed. By M. Wiberg, S. Culpepper, R. Janssen, J. González, & D. Molenaar, Springer International Publishing,  Switzerland, 297-304.

 

4.   Chang, J., Tsai, H., Su, Y.-H., and Lin, E.M.H. (2016). A three-parameter speeded item response model : estimation and application. In Quantitative Psychology Research – The 80th Annual Meeting of the Psychometric Society, Beijing, 2015, ed. By L.A. van der Ark, D.M. Bolt, W.-C. Wang, and J.A. Douglas, Springer International Publishing, Switzerland, 27-38.

      

5.   Tsai, H. and Chan, K.S. (2000). Comparison of two discretization methods for estimating continuous-time autoregressive models, in Statistics and Finance: An Inferface, 68-85. W.-S. Chan, W. K. Li  and  H. Tong eds. London: Imperial College Press.

     

6.   Tsai, H. and Chan, K.S. (1999). A new EM method for estimating continuous-time autoregressive models. Technical Report No 285, Department of Statistics & Actuarial Science, The University of Iowa.