Research page of Ruey S. Tsay
Research interest :
High-dimensional dependent data, Extreme value theory, Financial
High-frequency data analysis, Linear and nonlinear time series models,
Markov chain Monte Carlo methods, Risk management, and Machine learning.
Selected recent papers:
(a) Independent component analysis via distance covariance (with D. Matteson).
Journal of the American Statistical Association (2017), 112, 623-637.
(b) Modeling structured correlation matrices (with M. Pourahmadi)
Biometrika (2017), 104, 237-242.
(c) Clustering multiple time series with structural breaks (with Y. Wang).
Journal of Time Series Analysis (2019), to appear. (available on JTSA web)
(d) High-dimensional linear regression for dependent data with applications to now-casting (with Y. Han).
Statistica Sinica (2019), to appear. (available on Statistica Sinica web)
(e) Time evolution of income distribution with subgroup decompositions (with Y.T. Chen).
Econometric Reviews (2019+), to appear.
(f) Constrained factor models for high-dimensional matrix-variate time series (with Y. Chen and R. Chen).
Journal of the American Statistical Association (2019+), to appear.
(g) Spato-temporal models with space-time interaction and their applications to air pollution data (with S. Deb).
Statistica Sinica (2019+), to appear.
(h) Empirical dynamical quantiles for visualization of high-dimensional time series (with D. Pena and R. Zamar).
Technometrics (2019+), to appear.
(a) Analysis of Financial Time Series: Wiley, 2002
(b) A Course in Time
Series Analysis: Wiley, 2001
(edited with D. Pena & George Tiao)
(c) Analysis of Financial Time Series, 2nd Edition: Wiley, 2005
(d) Analysis of Financial Time Series, 3rd Edition: Wiley, 2010
(e) An Introduction to Analysis of Financial Data with R: Wiley, 2013
(f) Multivariate Time Series Analysis with R and Financial Applications: Wiley, 2014
(g) Nonlinear Time Series Analysis (with Rong Chen): Wiley, 2019