Informative dropout model for Poisson count data

Summary

In biomedical research with longitudinal designs, missing values due to intermittent non-response or premature withdrawal are usually non-ignorable in the sense that unobserved values are related to the patterns of missingness. This project investigates modelling strategies for repeated counts with non-ignorable missing using a Bayesian framework. Extended models that allow zero inflation and overdispersion in repeated counts will also be explored.

Supervisor(s)

Associate Professor Jennifer Chan

Research Location

School of Mathematics and Statistics

Program Type

Masters/PHD

Synopsis

non-ignorable dropout model for count data with zero-inflation and overdispersion

Additional Information

HDR Inherent Requirements

In addition to the academic requirements set out in the Science Postgraduate Handbook, you may be required to satisfy a number of inherent requirements to complete this degree. Example of inherent requirement may include:

- Confidential disclosure and registration of a disability that may hinder your performance in your degree;
- Confidential disclosure of a pre-existing or current medical condition that may hinder your performance in your degree (e.g. heart disease, pace-maker, significant immune suppression, diabetes, vertigo, etc.);
- Ability to perform independently and/or with minimal supervision;
- Ability to undertake certain physical tasks (e.g. heavy lifting);
- Ability to undertake observatory, sensory and communication tasks;
- Ability to spend time at remote sites (e.g. One Tree Island, Narrabri and Camden);
- Ability to work in confined spaces or at heights;
- Ability to operate heavy machinery (e.g. farming equipment);
- Hold or acquire an Australian driver’s licence;
- Hold a current scuba diving license;
- Hold a current Working with Children Check;
- Meet initial and ongoing immunisation requirements (e.g. Q-Fever, Vaccinia virus, Hepatitis, etc.)

You must consult with your nominated supervisor regarding any identified inherent requirements before completing your application.

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Keywords

longitudinal measurement, non-ignorable missing, zero-inflation, overdispersion

Opportunity ID

The opportunity ID for this research opportunity is: 1426

Other opportunities with Associate Professor Jennifer Chan