Go to  Advanced Search

Local parametric poisson models for fisheries data

Show full item record

Files in this item

Files Size Format Description   View
UBC_1988_A6_7 Y43.pdf 4.226Mb Adobe Portable Document Format   View/Open
Title: Local parametric poisson models for fisheries data
Author: Yee, Irene Mei Ling
Degree: Master of Science - MSc
Program: Statistics
Copyright Date: 1988
Subject Keywords Poisson processes;Bayesian statistical decision theory;Fisheries -- Statistical methods
Issue Date: 2010-09-09
Publisher University of British Columbia
Series/Report no. UBC Retrospective Theses Digitization Project [http://www.library.ubc.ca/archives/retro_theses/]
Abstract: Poisson process is a common model for count data. However, a global Poisson model is inadequate for sparse data such as the marked salmon recovery data that have huge extraneous variations and noise. An empirical Bayes model, which enables information to be aggregated to overcome the lack of information from data in individual cells, is thus developed to handle these data. The method fits a local parametric Poisson model to describe the variation at each sampling period and incorporates this approach with a conventional local smoothing technique to remove noise. Finally, the overdispersion relative to the Poisson model is modelled by mixing these locally smoothed, Poisson models in an appropriate way. This method is then applied to the marked salmon data to obtain the overall patterns and the corresponding credibility intervals for the underlying trend in the data.
Affiliation: Science, Faculty of
URI: http://hdl.handle.net/2429/28360
Scholarly Level: Graduate

This item appears in the following Collection(s)

Show full item record

UBC Library
1961 East Mall
Vancouver, B.C.
Canada V6T 1Z1
Tel: 604-822-6375
Fax: 604-822-3893