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

Article, Preprint English OPEN
Benjamin Hofner; Andreas Mayr; Matthias Schmid;
  • Publisher: Foundation for Open Access Statistics
  • Journal: Journal of Statistical Software (issn: 1548-7660)
  • Publisher copyright policies & self-archiving
  • Related identifiers: doi: 10.18637/jss.v074.i01
  • Subject: Statistics - Computation | high-dimensional data | additive models | prediction intervals | HA1-4737 | additive models; prediction intervals; high-dimensional data | Statistics

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 provid... View more
  • References (46)
    46 references, page 1 of 5

    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.

  • Related Research Results (1)
    Boosted Beta Regression (2013)
  • Metrics
Share - Bookmark