Statistics Seminar - 04/05/22

Apr 5 3:40 pm
Speaker

Dr. Yisu Jia, Assistant Professor of Statistics, Department of Mathematics and Statistics, University of North Florida

Title

Statistics Seminar Series (Hybrid)

Subtitle

Latent Gaussian Count Time Series

Physical Location

Allen 14

Digital Location

https://msstate.webex.com/msstate/j.php?MTID=m8ce377b82dde20f05e835b18379eff44

Abstract: This project develops the theory and methods for modeling a stationary count time series via Gaussian transformations. The techniques use a latent Gaussian process and a distributional transformation to construct stationary series with very flexible correlation features that can have any pre-specified marginal distribution, including the classical Poisson, generalized Poisson, negative binomial, and binomial structures. Gaussian pseudo-likelihood and implied Yule–Walker estimation paradigms, based on the autocovariance function of the count series, are developed via a new Hermite expansion. Particle filtering and sequential Monte Carlo methods are used to conduct likelihood estimation. Our estimation approaches are evaluated in a simulation study and the methods are used to analyze a count series of weekly retail sales.