publication . Article . Preprint . 2014

gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework

Benjamin Hofner; Andreas Mayr; Matthias Schmid;
Open Access English
  • Published: 07 Jul 2014 Journal: Journal of Statistical Software (issn: 1548-7660, Copyright policy)
  • Publisher: Foundation for Open Access Statistics
Abstract
Generalized additive models for location, scale and shape are a flexible class of regression models that allow to model multiple parameters of a distribution function, such as the mean and the standard deviation, simultaneously. With the R package gamboostLSS, we provide a boosting method to fit these models. Variable selection and model choice are naturally available within this regularized regression framework. To introduce and illustrate the R package gamboostLSS and its infrastructure, we use a data set on stunted growth in India. In addition to the specification and application of the model itself, we present a variety of convenience functions, including me...
Subjects
free text keywords: Statistics - Computation, high-dimensional data, additive models, prediction intervals, HA1-4737, additive models; prediction intervals; high-dimensional data, Statistics
46 references, page 1 of 4

Arnold F, Parasuraman S, Arokiasamy P, Kothari M (2009). “Nutrition in India. National Family Health Survey (NFHS-3), India, 2005-06.” Technical report, Mumbai: International Institute for Population Sciences, Calverton.

Belitz C, Brezger A, Kneib T, Lang S, Umlauf N (2015). BayesX: Software for Bayesian Binder H, Müller T, Schwender H, Golka K, Steffens M, Hengstler JG, Ickstadt K, Schumacher M (2012). “Cluster-Localized Sparse Logistic Regression for SNP Data.” Statistical Applications in Genetics and Molecular Biology, 11(4). doi:10.1515/1544-6115.1694.

Borghi E, De Onis M, Garza C, Van den Broeck J, Frongillo E, Grummer-Strawn L, Van Buuren S, Pan H, Molinari L, Martorell R, Onyango A, Martines J (2006). “Construction of the World Health Organization Child Growth Standards: Selection of Methods for Attained Growth Curves.” Statistics in Medicine, 25(2), 247-265. doi:10.1002/sim.2227.

Breiman L (2001). “Statistical Modeling: The Two Cultures.” Statistical Science, 16(3), 199-231. doi:10.1214/ss/1009213726.

Bühlmann P, Gertheiss J, Hieke S, Kneib T, Ma S, Schumacher M, Tutz G, Wang CY, Wang Z, Ziegler A (2014). “Discussion of “The Evolution of Boosting Algorithms” and “Extending Statistical Boosting”.” Methods of Information in Medicine, 53(6), 436-445. doi:10.3414/13100122.

Bühlmann P, Hothorn T (2007). “Boosting Algorithms: Regularization, Prediction and Model Fitting.” Statistical Science, 22(4), 477-522. doi:10.1214/07-sts242rej.

Bühlmann P, Yu B (2003). “Boosting with the L2 Loss: Regression and Classification.” Journal of the American Statistical Association, 98(462), 324-338. doi:10.1198/ 016214503000125.

Bühlmann P, Yu B (2007). “Sparse Boosting.” Journal of Machine Learning Research, 7, 1001-1024.

De Onis M (2006). “WHO Child Growth Standards Based on Length/Height, Weight and Age.” Acta Paediatrica, 95(S450), 76-85. doi:10.1111/j.1651-2227.2006.tb02378.x.

De Onis M, Monteiro C, Akre J, Clugston G (1993). “The Worldwide Magnitude of ProteinEnergy Malnutrition: An Overview from the WHO Global Database on Child Growth.” Bulletin of the World Health Organizationy, 71(6), 703-712. [OpenAIRE]

Eilers P, Marx B (1996). “Flexible Smoothing with B-Splines and Penalties.” Statistical Science, 11(2), 89-121. doi:10.1214/ss/1038425655. [OpenAIRE]

Fenske N, Burns J, Hothorn T, Rehfuess E (2013). “Understanding Child Stunting in India: A Comprehensive Analysis of Socio-Economic, Nutritional and Environmental Determinants Using Additive Quantile Regression.” PLOS ONE, 8(11), e78692. doi:10.1371/journal. pone.0078692. [OpenAIRE]

Fenske N, Kneib T, Hothorn T (2011). “Identifying Risk Factors for Severe Childhood Malnutrition by Boosting Additive Quantile Regression.” Journal of the American Statistical Association, 106(494), 494-510. doi:10.1198/jasa.2011.ap09272.

Hastie T (2007). “Comment: Boosting Algorithms: Regularization, Prediction and Model Fitting.” Statistical Science, 22(4), 513-515. doi:10.1214/07-sts242a.

Hastie T, Tibshirani R (1990). Generalized Additive Models. Chapman & Hall, London.

46 references, page 1 of 4
Related research
Abstract
Generalized additive models for location, scale and shape are a flexible class of regression models that allow to model multiple parameters of a distribution function, such as the mean and the standard deviation, simultaneously. With the R package gamboostLSS, we provide a boosting method to fit these models. Variable selection and model choice are naturally available within this regularized regression framework. To introduce and illustrate the R package gamboostLSS and its infrastructure, we use a data set on stunted growth in India. In addition to the specification and application of the model itself, we present a variety of convenience functions, including me...
Subjects
free text keywords: Statistics - Computation, high-dimensional data, additive models, prediction intervals, HA1-4737, additive models; prediction intervals; high-dimensional data, Statistics
46 references, page 1 of 4

Arnold F, Parasuraman S, Arokiasamy P, Kothari M (2009). “Nutrition in India. National Family Health Survey (NFHS-3), India, 2005-06.” Technical report, Mumbai: International Institute for Population Sciences, Calverton.

Belitz C, Brezger A, Kneib T, Lang S, Umlauf N (2015). BayesX: Software for Bayesian Binder H, Müller T, Schwender H, Golka K, Steffens M, Hengstler JG, Ickstadt K, Schumacher M (2012). “Cluster-Localized Sparse Logistic Regression for SNP Data.” Statistical Applications in Genetics and Molecular Biology, 11(4). doi:10.1515/1544-6115.1694.

Borghi E, De Onis M, Garza C, Van den Broeck J, Frongillo E, Grummer-Strawn L, Van Buuren S, Pan H, Molinari L, Martorell R, Onyango A, Martines J (2006). “Construction of the World Health Organization Child Growth Standards: Selection of Methods for Attained Growth Curves.” Statistics in Medicine, 25(2), 247-265. doi:10.1002/sim.2227.

Breiman L (2001). “Statistical Modeling: The Two Cultures.” Statistical Science, 16(3), 199-231. doi:10.1214/ss/1009213726.

Bühlmann P, Gertheiss J, Hieke S, Kneib T, Ma S, Schumacher M, Tutz G, Wang CY, Wang Z, Ziegler A (2014). “Discussion of “The Evolution of Boosting Algorithms” and “Extending Statistical Boosting”.” Methods of Information in Medicine, 53(6), 436-445. doi:10.3414/13100122.

Bühlmann P, Hothorn T (2007). “Boosting Algorithms: Regularization, Prediction and Model Fitting.” Statistical Science, 22(4), 477-522. doi:10.1214/07-sts242rej.

Bühlmann P, Yu B (2003). “Boosting with the L2 Loss: Regression and Classification.” Journal of the American Statistical Association, 98(462), 324-338. doi:10.1198/ 016214503000125.

Bühlmann P, Yu B (2007). “Sparse Boosting.” Journal of Machine Learning Research, 7, 1001-1024.

De Onis M (2006). “WHO Child Growth Standards Based on Length/Height, Weight and Age.” Acta Paediatrica, 95(S450), 76-85. doi:10.1111/j.1651-2227.2006.tb02378.x.

De Onis M, Monteiro C, Akre J, Clugston G (1993). “The Worldwide Magnitude of ProteinEnergy Malnutrition: An Overview from the WHO Global Database on Child Growth.” Bulletin of the World Health Organizationy, 71(6), 703-712. [OpenAIRE]

Eilers P, Marx B (1996). “Flexible Smoothing with B-Splines and Penalties.” Statistical Science, 11(2), 89-121. doi:10.1214/ss/1038425655. [OpenAIRE]

Fenske N, Burns J, Hothorn T, Rehfuess E (2013). “Understanding Child Stunting in India: A Comprehensive Analysis of Socio-Economic, Nutritional and Environmental Determinants Using Additive Quantile Regression.” PLOS ONE, 8(11), e78692. doi:10.1371/journal. pone.0078692. [OpenAIRE]

Fenske N, Kneib T, Hothorn T (2011). “Identifying Risk Factors for Severe Childhood Malnutrition by Boosting Additive Quantile Regression.” Journal of the American Statistical Association, 106(494), 494-510. doi:10.1198/jasa.2011.ap09272.

Hastie T (2007). “Comment: Boosting Algorithms: Regularization, Prediction and Model Fitting.” Statistical Science, 22(4), 513-515. doi:10.1214/07-sts242a.

Hastie T, Tibshirani R (1990). Generalized Additive Models. Chapman & Hall, London.

46 references, page 1 of 4
Related research
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publication . Article . Preprint . 2014

gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework

Benjamin Hofner; Andreas Mayr; Matthias Schmid;