Estimation and inference with complex count data from fisheries surveys, including over-dispersion, many nuisance parameters, and correlation

Wang, Shijia (2015) Estimation and inference with complex count data from fisheries surveys, including over-dispersion, many nuisance parameters, and correlation. Masters thesis, Memorial University of Newfoundland.

[English] PDF - Accepted Version Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Download (1MB)

Abstract

We study methods to estimate regression and variance parameters for over-dispersed and correlated count data from highly stratified surveys. A challenge with such data is the large number of nuisance parameters which leads to computational issues and biased statistical inferences. We develop a profile generalized estimating equation (GEE) method that is more computationally efficient and compare it to marginal maximum likelihood (MLE) and restricted MLE (REML) methods. We use REML to address bias and inaccurate confidence intervals because of many nuisance parameters. The marginal MLE and REML approaches involve intractable integrals and we used a new R package that is designed for estimating complex nonlinear models that may include random effects. We conduct simulation analyses and conclude that the REML method is the better approach among the three methods we investigate. Our applications involve counts of fish catches from highly-stratified research surveys. In the first application, we estimate the day and night (diel) effect for three species from bottom trawl research surveys. In the second application, we estimate the diel and vessel effects of two different snow crab surveys.

Actions (login required)

View Item