Dynamic Models for Multivariate Time Series of Counts, with Applications
3:30 PM – 4:30 PM, Thursday November 7th, 2013
Marrs McLean Science Building, Room GL 51
Nalini Ravishanker, Ph.D.
Department of Statistics,
University of Connecticut
Abstract
Time series counts data occurs in many disciplines and there is considerable interest in developing
accurate methods for modeling and prediction. This talk describes dynamic models for univariate and
multivariate time series of counts, incorporating time dependence and dependence between the
components of the series. Dynamic generalized linear models and nonlinear models, including a
hierarchical setup to handle subject-specific estimation, are described and a Bayesian framework for
inference is discussed. The approach is illustrated using an application from ecology for modeling
gastropod abundance.
A reception with refreshments will be held from 3:00 PM – 3:30 PM in the lobby of the Statistical
Science Department, on the first floor of Marrs McLean Science.