Identifying Risk Factors for Severe Childhood Malnutrition by Boosting Additive Quantile Regression

We investigated the risk factors for childhood malnutrition in India based on the 2005/2006 Demographic and Health Survey by applying a novel estimation technique for additive quantile regression. Ordinary linear and generalized linear regression models relate the mean of a response variable to a li...

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Vydané v:	Journal of the American Statistical Association Ročník 106; číslo 494; s. 494 - 510
Hlavní autori:	Fenske, Nora, Kneib, Thomas, Hothorn, Torsten
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Alexandria, VA American Statistical Association 01.06.2011 Assoc Taylor & Francis Ltd
Predmet:	Additives Adultery Applications Applications and Case Studies Biology, psychology, social sciences Child health Childhood Childrens health Covariance Data smoothing Datasets Developing countries Empirical research Estimation Exact sciences and technology General topics Growth retardation Health surveys LDCs Linear inference, regression Malnutrition Mathematical models Mathematics Medical sciences Methodology Modeling Novels Nutritional status Predisposing factors Probability and statistics Property Quantile regression Quantiles Regression analysis Responses Risk Risk factors Sciences and techniques of general use Statistics India Biometrics Generalized linear model Additive models Developing countries Penalized splines Covariate Quantile regression Additive model Linear model Penalty method Semiparametric model Spline approximation Numerical approximation Medical science Linear combination Distribution function Selection method Sample survey Child Quantile Nutritional status Variable selection Human Functional gradient boosting Linear regression Model selection Average Statistical estimation Mean estimation Functional Statistical method Statistical regression Selection problem Model choice Regression model Non linear model Risk factor Stunting
ISSN:	0162-1459, 1537-274X
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Popis
Shrnutí:	We investigated the risk factors for childhood malnutrition in India based on the 2005/2006 Demographic and Health Survey by applying a novel estimation technique for additive quantile regression. Ordinary linear and generalized linear regression models relate the mean of a response variable to a linear combination of covariate effects, and, as a consequence, focus on average properties of the response. The use of such a regression model for analyzing childhood malnutrition in developing or transition countries implies that the estimated effects describe the average nutritional status. However, it is of even greater interest to analyze quantiles of the response distribution, such as the 5% or 10% quantile, which relate to the risk of extreme malnutrition. Our investigation is based on a semiparametric extension of quantile regression models where different types of nonlinear effects are included in the model equation, leading to additive quantile regression. We addressed the variable selection and model choice problems associated with estimating such an additive quantile regression model using a novel boosting approach. Our proposal allows for data-driven determination of the amount of smoothness required for the nonlinear effects and combines model choice with an automatic variable selection property. In an empirical evaluation, we compared our boosting approach with state-of-the-art methods for additive quantile regression. The results suggest that boosting is an appropriate tool for estimation and variable selection in additive quantile regression models and helps to identify yet unknown risk factors for childhood malnutrition. This article has supplementary material online.
Bibliografia:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23
ISSN:	0162-1459 1537-274X
DOI:	10.1198/jasa.2011.ap09272