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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