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Boosting Structured Additive Quantile Regression for Longitudinal Childhood Obesity Data
Vise andre og tillknytning
2013 (engelsk)Inngår i: The International Journal of Biostatistics, ISSN 1557-4679, E-ISSN 1557-4679, Vol. 9, nr 1, s. 1-18Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Childhood obesity and the investigation of its risk factors has become an important public health issue. Our work is based on and motivated by a German longitudinal study including 2,226 children with up to ten measurements on their body mass index (BMI) and risk factors from birth to the age of 10 years. We introduce boosting of structured additive quantile regression as a novel distribution-free approach for longitudinal quantile regression. The quantile-specific predictors of our model include conventional linear population effects, smooth nonlinear functional effects, varying-coefficient terms, and individual-specific effects, such as intercepts and slopes. Estimation is based on boosting, a computer intensive inference method for highly complex models. We propose a component-wise functional gradient descent boosting algorithm that allows for penalized estimation of the large variety of different effects, particularly leading to individual-specific effects shrunken toward zero. This concept allows us to flexibly estimate the nonlinear age curves of upper quantiles of the BMI distribution, both on population and on individual-specific level, adjusted for further risk factors and to detect age-varying effects of categorical risk factors. Our model approach can be regarded as the quantile regression analog of Gaussian additive mixed models (or structured additive mean regression models), and we compare both model classes with respect to our obesity data.

sted, utgiver, år, opplag, sider
2013. Vol. 9, nr 1, s. 1-18
Emneord [en]
longitudinal quantile regression; additive mixed models; body mass index; overweight
HSV kategori
Identifikatorer
URN: urn:nbn:se:su:diva-99519DOI: 10.1515/ijb-2012-0035ISI: 000329433300001OAI: oai:DiVA.org:su-99519DiVA, id: diva2:687151
Merknad

AuthorCount: 4

Funding agencies:

German Federal Ministry for Education, Science, Research and Technology 01 EG 9705/2, 01 EG 9732;  German Federal Ministry of Environment (IUF) FKS 20462296;  Kompetenznetz Adipositas (Competence Network Obesity);  Federal Ministry of Education and Research FKZ: 01GI0826; Munich Center of Health Sciences (MC-Health)  

Tilgjengelig fra: 2014-01-13 Laget: 2014-01-13 Sist oppdatert: 2017-12-06bibliografisk kontrollert

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