Prediction intervals for future BMI values of individual children - A non-parametric approach by quantile boosting

BMC Medical Research Methodology

Volumn 12, Issue , 2012, Pages

  Mayr, Andreas ^a   Hothorn, Torsten ^b   Fenske, Nora ^b  

^a UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

^b UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ADOLESCENT; AGE DISTRIBUTION; ARTICLE; BODY MASS; CHILD; FEMALE; GERMANY; HUMAN; MALE; NONPARAMETRIC TEST; PRESCHOOL CHILD; REFERENCE VALUE; REGRESSION ANALYSIS; SEX RATIO; STATISTICAL MODEL;

ADOLESCENT; AGE DISTRIBUTION; BODY MASS INDEX; CHILD; CHILD, PRESCHOOL; FEMALE; GERMANY; HUMANS; LIKELIHOOD FUNCTIONS; MALE; MODELS, STATISTICAL; REFERENCE VALUES; REGRESSION ANALYSIS; SEX DISTRIBUTION; STATISTICS, NONPARAMETRIC;

EID: 84856094557     PISSN: None     EISSN: 14712288     Source Type: Journal    
DOI: 10.1186/1471-2288-12-6     Document Type: Article

References (31)

1. The Obesity Epidemic: Analysis of Past and Projected Future Trends in Selected OECD Countries. Sassi F, Devaux M, Cecchini M, Rusticelli E, OECD Health Working Papers 2009 45
2. Childhood Obesity, Prevalence and Prevention. Dehghan M, Akhtar-Danesh N, Merchant A, Nutrition Journal 2005 4 24 10.1186/1475-2891-4-24 16138930 (Pubitemid 41309077)
3. Utility of Childhood BMI in the Prediction of Adulthood Disease: Comparison of National and International References. Jansen I, Katzmarzykt P, Srinivasan S, Chenl W, Malina R, Bouchard C, Berenson G, Obesity Research 2005 13 1106 1115 10.1038/oby.2005.129 15976154
4. Predicting Obesity in Young Adulthood from Childhood and Parental Obesity. Whitaker R, Wright J, Pepe M, Seidel K, Dietz W, New England Journal of Medicine 1997 337 13 869 873 10.1056/NEJM199709253371301 9302300 (Pubitemid 27408568)
5. LISA-plus Study Group, 1998 Information about the study is available at http://www.helmholtz-muenchen.de/epi/arbeitsgruppen/umweltepidemiologie/ projects-projekte/lisa-plus/index.html
6. Early Life Risk Factors for Obesity in Childhood: Cohort Study. Reilly JJ, Armstrong J, Dorosty AR, Emmett PM, Ness A, Rogers I, Steer C, Sherriff A, British Medical Journal 2005 330 1357 1364 10.1136/bmj.38470.670903.E0 15908441 (Pubitemid 40839561)
7. Risk Factors for Childhood Overweight: Shift of the Mean Body Mass Index and Shift of the Upper Percentiles: Results From a Cross-Sectional Study. Beyerlein A, Toschke AM, von Kries R, International Journal of Obesity 2010 34 4 642 648 10.1038/ijo.2009.301 20084072
8. Alternative Regression Models to Assess Increase in Childhood BMI. Beyerlein A, Fahrmeir L, Mansmann U, Toschke A, BMC Medical Research Methodology 2008 8 59
9. Detection of Risk Factors for Obesity in Early Childhood with Quantile Regression Methods for Longitudinal Data. Fenske N, Fahrmeir L, Rzehak P, Höhle M, Technical Report, Department of Statistics, University of Munich 2008 038
10. Generalized Additive Models for Location, Scale and Shape (with Discussion). Rigby RA, Stasinopoulos DM, Applied Statistics 2005 54 507 554 10.1111/j.1467-9876.2005.00510.x (Pubitemid 40659551)
11. GAMLSS for High-Dimensional Data-a Flexible Approach Based on Boosting. Mayr A, Fenske N, Hofner B, Kneib T, Schmid M, Journal of the Royal Statistical Society, Series C (Applied Statistics) 2012 [To appear]
12. Quantile Regression Forests. Meinshausen N, Journal Machine Learning Research 2006 7 983 999 (Pubitemid 43902307)
13. Identifying Risk Factors for Severe Childhood Malnutrition by Boosting Additive Quantile Regression. Fenske N, Kneib T, Hothorn T, Journal of the American Statistical Association 2011 106 494 494 510 10.1198/jasa.2011.ap09272
14. Koenker R, Quantile Regression New York: Cambridge University Press 2005
15. Quantile Smoothing Splines. Koenker R, Ng P, Portnoy S, Biometrika 1994 81 4 673 680 10.1093/biomet/81.4.673
16. Random Forests. Breiman L, Machine Learning 2001 45 5 32 10.1023/A:1010933404324 (Pubitemid 32933532)
17. Greedy Function Approximation: A Gradient Boosting Machine. Friedman JH, Annals of Statistics 2001 29 1189 1232 (Pubitemid 33405972)
18. Regression Shrinkage and Selection via the Lasso. Tibshirani R, J Roy Statist Soc Ser B 1996 58 267 288
19. Boosting Algorithms: Regularization, Prediction and Model Fitting. Bühlmann P, Hothorn T, Journal of Statistical Science 2007 22 4 477 505 10.1214/07-STS242
20. Biased Versus Unbiased Estimation. Efron B, Advances in Mathematics 1975 16 259 277 10.1016/0001-8708(75)90114-0
21. Regression, Prediction and Shrinkage. Copas JB, Royal Statistical Society, Series B 1983 45 311 354
22. Hastie T, Tibshirani R, Friedman J, The Elements of Statistical Learning: Data Mining, Inference and Prediction Springer 2 2009
23. Comment: Boosting Algorithms: Regularization, Prediction and Model Fitting. Hastie T, Journal of Statistical Science 2007 22 4 513 515 10.1214/07-STS242A
24. R Development Core Team, R: A Language and Environment for Statistical Computing R Foundation for Statistical Computing, Vienna, Austria 2009 http://www.R-project.org. [ISBN 3-900051-07-0]
25. Hothorn T, Bühlmann P, Kneib T, Schmid M, Hofner B, mboost: Model-Based Boosting 2010 http://R-forge.R-project.org/projects/mboost. [R package version 2.1-0]
26. Model-based Boosting 2.0. Hothorn T, Bühlmann P, Kneib T, Schmid M, Hofner B, Journal of Machine Learning Research 2010 11 2109 2113
27. Meinshausen N, quantregForest: Quantile Regression Forests 2007 [R package version 0.2-2]
28. Conditional Growth Charts. Wei Y, He X, Annals of Statistics 2006 34 2069 10.1214/009053606000000623
29. Quantile Regression Methods for Reference Growth Charts. Wei Y, Pere A, Koenker R, He X, Statistics in Medicine 2006 25 8 1369 1382 10.1002/sim.2271 16143984
30. Variable Selection and Model Choice in Geoadditive Regression Models. Kneib T, Hothorn T, Tutz G, Biometrics 2009 65 2 626 634 [Including the web-based supplementary] 10.1111/j.1541-0420.2008.01112.x 18759832
31. Quantile Regression for Longitudinal Data. Koenker R, Journal of Multivariate Analysis 2004 91 74 89 10.1016/j.jmva.2004.05.006 (Pubitemid 39007501)