2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPTM-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
SessionSPTM-7: Estimation Theory and Methods 1
LocationGather.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
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.