Generalized Linear Models with Nonignorable Missing Covariates

In this thesis, we present an overview of generalized linear models (GLMs) for binary and count data with missing covariates when the missing data mechanism is nonignorable. We use the maximum likelihood method to estimate the parameters in GLMs. We study a set of ML estimating equations for fitting regression models to binary and Poisson data with missing covariates.Simulations were carried out to observe the behaviour of the MLEs under both correctly specified and misspecified structures. Our simulation study shows that the ML method generally provides unbiased and efficient estimators under correctly specified models, whereas a misspecified model provides biased and inefficient estimators. It also indicates that for small sample size, the empirical coverage probabilities of the parameter estimates are a bit apart from the nominal 95% level. Also, the average lengths of the confidence intervals for the regression parameters tend to be smaller for larger sample size.

Name thesis:degree_name

Master of Science (M.Sc.)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Probability and Statistics

Grantor dc:publisher

Carleton University

Year dc:date.issued

2017

Author dc:creator

• Lopa, Sonia Afroje

Statement dc:rights

• Copyright © 2017 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.

Language dc:language.iso

en

DOI

10.22215/etd/2017-12179

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/38673