Inferences on Accelerated Life Model with Various Types of Censored Data

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dc.contributor.advisorRen, Joan Jian-Jianen_US
dc.contributor.authorLyu, Yimingen_US
dc.contributor.departmentMathematicsen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2021-07-07T05:36:04Z
dc.date.available2021-07-07T05:36:04Z
dc.date.issued2020en_US
dc.description.abstractIn survival data analysis, the Accelerated Life Model (ALM) is one of the most widely used statistical models. However, until now there has been little work done in statistical literature on the ALM for complicated types of censored data, such as doubly censored data, interval censored data, etc., while these types of censored data are frequently encountered in practice. Some of the relevant existing works treat the log form of ALM as a linear regression model and make statistical inferences on the ALM based on the least squares method. In this dissertation, we first use a simulation study to demonstrate that the log form of ALM is not a linear regression model, which motivates us to develop estimation methods for the ALM with various types of censored survival data via the \textit{weighted empirical likelihood} approach (Ren, 2001, 2008). We develop a weighted empirical likelihood-based estimation method for the ALM in a unified way for various types of censored data. We also provide an algorithm to implement the estimation method and present some simulation results.en_US
dc.identifierhttps://doi.org/10.13016/53ko-yqq5
dc.identifier.urihttp://hdl.handle.net/1903/27239
dc.language.isoenen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pquncontrolledAccelerated Life Modelen_US
dc.subject.pquncontrolledCensored Dataen_US
dc.subject.pquncontrolledWeighted Empirical Likelihooden_US
dc.titleInferences on Accelerated Life Model with Various Types of Censored Dataen_US
dc.typeDissertationen_US

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