Lambert, Amanda (2007) Generalized linear mixed model analysis using quasi-likelihood. Masters thesis, Memorial University of Newfoundland.
- 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.
When investigating the relationship between two or more variables, regression is a commonly used method of analysis. Linear regression, in particular, is used when the expected value of the response is a linear function of the explanatory variables. If it is not a linear function, generalized linear regression is used. Furthermore, when the data is not independent, mixed models are used. There are various ways to analyze linear mixed models and generalized linear mixed models. In this thesis, we focus on the moment method of analysis, simulated approaches and the quasi-likelihood method of analysis. Analysis is conducted on simulated data for a linear mixed model, simulated data for a generalized linear mixed model and on a real data, set. The real data set is a clustered data set of the number of times a person visits a physician in a given year.
|Item Type:||Thesis (Masters)|
|Additional Information:||Includes bibliographical references (leaves 92-93)|
|Department(s):||Science, Faculty of > Mathematics and Statistics|
|Library of Congress Subject Heading:||Estimation theory; Linear models (Statistics); Regression analysis|
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