Infinitely
Divisible Time Series Models 
Xuefeng Li
Department of Statistics
University of Pennsylvania
Monday, 7 April 2003
4:10 PM
E202 Engineering Building
ABSTRACT
Motivated from a project of analyzing call center data, time series
models with infinitely divisible marginal distributions are studied.
Existing models, though have a form similar to the classical ARMA
model, have great restrictions. In this work we proposed two new
constructions. The first one comes from the construction of multivariate
random variables with infinitely divisible margins and gives more
flexible moving average structure. The second one is based on the
integration of Gamma random fields and gives continuous stationary
stochastic processes with Gamma margins. Most of the properties
about these new constructions carry over to the family of infinitely
divisible distributions. Estimation procedures as well as their
asymptotic properties are investigated. Open questions and future
research directions are discussed.
This is a joint work with Dr. Lawrence Brown and Dr. Robert Wolpert.
Refreshments will be served at 3:45 p.m. in Room 008 of the Statistics
Building.
