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Activity Number: 576
Type: Invited
Date/Time: Wednesday, August 12, 2015 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #314270
Title: Bayesian Lattice Filters for Time-Varying Autoregression and Time-Frequency Analysis
Author(s): Scott H. Holan* and Wen-Hsi Yang and Christopher K. Wikle
Companies: University of Missouri and CSIRO and University of Missouri
Keywords: Bayesian ; Nonstationary ; Partial autocorrelation ; Spectral density ; Time series ; Time-varying spectral density
Abstract:

Modeling nonstationary processes is of paramount importance to many scientific disciplines. Consequently, we propose a novel approach to model-based time-frequency estimation using time-varying autoregressive models. In this context, we take a fully Bayesian approach and allow both the autoregressive coefficients and innovation variance to vary over time. Importantly, our estimation method uses the lattice filter and is cast within the partial autocorrelation domain. The marginal posterior distributions are of standard form and, as a convenient by-product of our estimation method, our approach avoids undesirable matrix inversions. As such, estimation is extremely computationally efficient and stable. To illustrate the effectiveness of our approach, we conduct a comprehensive simulation study that compares our method with other competing methods and find that, in most cases, our approach performs superior in terms of average squared error between the estimated and true time-varying spectral density. Lastly, we demonstrate our methodology through several modeling applications; including time-frequency estimation of insect communication signals and macroeconomic data.


Authors who are presenting talks have a * after their name.

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