| Paper ID | SPTM-7.5 |
| Paper Title |
PARAMETER ESTIMATION FOR STUDENT'S t VAR MODEL WITH MISSING DATA |
| Authors |
Rui Zhou, Junyan Liu, The Hong Kong University of Science and Technology, Hong Kong SAR China; Sandeep Kumar, Indian Institute of Technology, Delhi, India; Daniel Palomar, The Hong Kong University of Science and Technology, Hong Kong SAR China |
| Session | SPTM-7: Estimation Theory and Methods 1 |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 13:00 - 13:45 |
| Presentation Time: | Wednesday, 09 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Signal Processing Theory and Methods: [SSP] Statistical Signal Processing |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
The vector autoregressive (VAR) models provide a significant tool for multivariate time series analysis. Most existing works on VAR modeling are based on the multivariate Gaussian distribution. However, heavy-tailed distributions are suggested more reasonable for capturing the real-world phenomena, like the presence of outliers and a stronger possibility of extreme values. Furthermore, missing values in observed data is a real problem, which typically happens during the data observation or recording process. In this paper, we propose an algorithmic framework to estimate the parameters of a VAR model with heavy-tailed Student’s t distributed innovations from in- complete data based on the stochastic approximation expectation maximization (SAEM) algorithm coupled with a Markov Chain Monte Carlo (MCMC) procedure. Extensive experiments with synthetic data corroborate our claims. |
