Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.698330
Title: Maximum rank correlation estimation for generalized varying-coefficient models with unknown monotonic link function
Author: Li, Xiang
Awarding Body: University of York
Current Institution: University of York
Date of Award: 2016
Availability of Full Text:
Access from EThOS:
Access from Institution:
Abstract:
Generalized varying coefficient models (GVCMs) form a family of statistical utilities that are applicable to real world questions for exploring associations between covariates and response variables. Researchers frequently fit GVCMs with particular link transformation functions. It is vital to recognize that to invest a model with a wrong link could provide extremely misleading knowledge. This thesis intends to bypass the actual form of the link function and explore a set of GVCMs whose link functions are monotonic. With the monotonicity being secured, this thesis endeavours to make use of the maximum rank correlation idea and proposes a maximum rank correlation estimation (MRCE) method for GVCMs. In addition to the introduction of MRCE, this thesis further extends the consideration to Generalized Semi-Varying Coefficient Models (GSVCMs), Panel data, simulations and empirical studies.
Supervisor: Zhang, Wenyang Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.698330  DOI: Not available
Share: