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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Bibliographic Details
Published in:Journal of the American Statistical Association Vol. 106; no. 494; pp. 494 - 510
Main Authors: Fenske, Nora, Kneib, Thomas, Hothorn, Torsten
Format: Journal Article
Language:English
Published: Alexandria, VA American Statistical Association 01.06.2011
Assoc
Taylor & Francis Ltd
Subjects:
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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Summary: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.
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ISSN:0162-1459
1537-274X
DOI:10.1198/jasa.2011.ap09272