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. Ho, H.-C., Chen, H.-Y., and Tsai, H. (2020). Estimation and prediction of conditional tail expectation for long-horizon returns. Statistica Sinica, in press.
  2. Yu, T.-H., Tsai, H., and Rachinger, H. (2020). Approximate maximum likelihood estimation of a threshold diffusion process, Computational Statistics and Data Analysis, 106823.
  3. Tsai, H., Rachinger, H., and Chan, K.S. (2018). Inference of bivariate long-memory aggregate time series, Statistica Sinica, 28, 399-421.
  4. 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. 
  5. 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.
  6. 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.
  7. 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.
  8. Hassler, U. and Tsai, H. (2013). Asymptotic behavior of temporal aggregates in the frequency domain. Journal of Time Series Econometrics, 5, 47-60.
  9. Chan, K.S. and Tsai, H. (2012). Inference of seasonal long-memory aggregate time series. Bernoulli, 18, 1448-1464.
  10. 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.
  11. Tsai, H. and Tsay, R.S. (2010). Constrained factor models. Journal of the American Statistical Association, 105, 1593-1605.
  12. 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.
  13. 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.
  14. Tsai, H. (2009). On continuous-time autoregressive fractionally integrated moving average processes. Bernoulli, 15,178-194.
  15. Tsai, H. and Chan, K.S. (2008). A note on inequality constraints in the GARCH Model. Econometric Theory , 24, 823-828.
  16. Tsai, H. and Chan, K.S. (2007). A note on non-negative ARMA processes. Journal of Time Series Analysis, 28, 350-360.
  17. Tsai, H. (2006). Quasi-maximum likelihood estimation of long-memory limiting aggregate processes. Statistica Sinica, 16, 213-226.
  18. 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.
  19. 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.
  20. Tsai, H. and Chan, K.S. (2005). Temporal aggregation of stationary and nonstationary discrete-time processes. Journal of Time Series Analysis, 26, 613-624.
  21. Tsai, H. and Chan, K.S. (2005). Temporal aggregation of stationary and non-stationary continuous-time processes. Scandinavian Journal of Statistics, 32, 583-597.
  22. 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.
  23. 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.
  24. Tsai, H. and Chan, K.S. (2002). A note on testing for nonlinearity with partially observed time series. Biometrika, 89, 245-50.
  25. Tsai, H. and Chan, K.S. (2000). Testing for nonlinearity with partially observed time series. Biometrika, 87, 805-821.
  26. 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. 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.

          

2. 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.

 

3.   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.

      

4.   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.

     

5.   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.