Full metadata record
|dc.contributor||Department of Applied Mathematics||en_US|
|dc.contributor.advisor||Zhao, Xingqiu (AMA)||-|
|dc.creator||Liu, Kin Yat||-|
|dc.publisher||Hong Kong Polytechnic University||-|
|dc.rights||All rights reserved||en_US|
|dc.title||Robust estimation for longitudinal data with informative observation times||en_US|
|dcterms.abstract||In this thesis, we focus on regression analysis of longitudinal data that often occur in medical follow-up studies and observational investigations. The analysis of these data involves two processes. One is the underlying recurrent event process of interest and the other is the observation process that controls observation times. Most of the existing methods, however, rely on some restrictive models or assumptions such as the Poisson assumption. For this, we propose a more general and robust estimation approach for regression analysis of longitudinal data with related observation times. The asymptotic properties of the proposed estimators are established and numerical studies indicate that the proposed method works well for practical situations.||en_US|
|dcterms.extent||53 leaves ; 30 cm||en_US|
|dcterms.LCSH||Medicine -- Research -- Statistical methods.||en_US|
|dcterms.LCSH||Hong Kong Polytechnic University -- Dissertations||en_US|
Files in This Item:
|b28068270.pdf||For All Users||814.74 kB||Adobe PDF||View/Open|
As a bona fide Library user, I declare that:
- I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
- I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
- I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.
Please use this identifier to cite or link to this item: