Fenske, Nora (2012): Structured additive quantile regression with applications to modelling undernutrition and obesity of children. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics 

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Abstract
Quantile regression allows to model the complete conditional distribution of a response variable  expressed by its quantiles  depending on covariates, and thereby extends classical regression models which mainly address the conditional mean of a response variable. The present thesis introduces the generic model class of structured additive quantile regression. This model class combines quantile regression with a structured additive predictor and thereby enables a variety of covariate effects to be flexibly modelled. Among other components, the structured additive predictor comprises smooth nonlinear effects of continuous covariates and individualspecific effects which are particularly important in longitudinal data settings. Furthermore, this thesis gives an extensive overview of existing approaches for parameter estimation in structured additive quantile regression models. These approaches are structured into distributionfree and distributionbased approaches as well as related model classes. Each approach is systematically discussed with regard to the four previously defined criteria, (i) which different components of the generic predictor can be estimated, (ii) which properties can be attributed to the estimators, (iii) if variable selection is possible, and, finally, (iv) if software is available for practical applications. The main methodological development of this thesis is a boosting algorithm which is presented as an alternative estimation approach for structured additive quantile regression. The discussion of this innovative approach with respect to the four criteria points out that quantile boosting involves great advantages regarding almost all criteria  in particular regarding variable selection. In addition, the results of several simulation studies provide a practical comparison of boosting with alternative estimation approaches. From the beginning of this thesis, the development of structured additive quantile regression is motivated by two relevant applications from the field of epidemiology: the investigation of risk factors for child undernutrition in India (by a crosssectional study) and for child overweight and obesity in Germany (by a birth cohort study). In both applications, extreme quantiles of the response variables are modelled by structured additive quantile regression and estimated by quantile boosting. The results are described and discussed in detail.
Item Type:  Thesis (Dissertation, LMU Munich) 

Subjects:  600 Natural sciences and mathematics 600 Natural sciences and mathematics > 510 Mathematics 
Faculties:  Faculty of Mathematics, Computer Science and Statistics 
Language:  English 
Date Accepted:  30. October 2012 
1. Referee:  Fahrmeir, Ludwig 
Persistent Identifier (URN):  urn:nbn:de:bvb:19151610 
MD5 Checksum of the PDFfile:  a1760b787c3ca9694a709c38c11c5dcf 
Signature of the printed copy:  0001/UMC 20858 
ID Code:  15161 
Deposited On:  07. Jan 2013 12:44 
Last Modified:  20. Jul 2016 10:31 