Davis, R.
, Dunsmuir, W.
and Streett, S.
(2005),
Maximum Likelihood Estimation for an Observation Driven Model for Poisson Counts, Methodology And Computing In Applied Probability
(Accessed April 26, 2025)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Maximum Likelihood Estimation for an Observation Driven Model for Poisson Counts
Published
Author(s)
Richard D. Davis, William T. Dunsmuir,
Abstract
This paper is concerned with an observation driven model for time series of counts whose conditional distribution given past observations follows a Poisson distribution. This class of models is capable of modeling a wide range of dependence structures and is readily estimated using an approximation to the likelihood function. Recursive formulae for carrying out maximum likelihood estimation are provided and the technical components required for establishing a central limit theorem of the maximum likelihood estimates are given in a special case.Citation
Methodology And Computing In Applied Probability
Volume
7
Pub Type
Journals
Keywords
asymptotic distribution of MLE, observation-driven model, Poisson-valued time series
Citation
Issues
If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.
Created January 27, 2005, Updated October 12, 2021