Modeling time series of count data

The focus of this thesis is on modeling time series of count data. We consider an extension of linear Gaussian state space models - parameter driven models in which th e mean function of a time series of observed counts {Yt} is specified bv a linear predictor modified by a 'latent process’. As in linear regression with correlated errors, there is a need for model diagnostic and identification techniques to decide if it is necessary to include a latent process in the specification of the mean of the Poisson counts and, if so, is there any evidence of autocorrelation in such a process.

Name thesis:degree_name

Doctor of Philosophy (Ph.D.)

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Statistics

Grantor dc:publisher

Colorado State University. Libraries

Year dc:date.issued

2002

Authors dc:creator

• Wang, Ying, author
• Davis, Richard A., advisor
• Boes, Duane C., committee member

• statistics

Statement dc:rights

• Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.

Language dc:language.iso

eng, English

Identifier URI

https://doi.org/10.25675/3.025767

OAI identifier oai:identifier

oai:mountainscholar.org:10217/242910