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mboost Illustrations

By Torsten Hothorn and Peter Bühlmann

Abstract

This document reproduces the data analyses presented in Bühlmann and Hothorn (2007). For a description of the theory behind applications shown here we refer to the original manuscript. The results differ slightly due to technical changes or bugfixes in mboost that have been implemented after the paper was printed. Most important, gamboost uses penalized B-splines instead of smoothing splines as baselearners. The computations are much faster and the results differ only slightly (Schmid and Hothorn, 2008). Illustration: Prediction of total body fat Garcia et˜al. (2005) report on the development of predictive regression equations for body fat content by means of p = 9 common anthropometric measurements which were obtained for n = 71 healthy German women. In addition, the women’s body composition was measured by Dual Energy X-Ray Absorptiometry (DXA). This reference method is very accurate in measuring body fat but finds little applicability in practical environments, mainly because of high costs and the methodological efforts needed. Therefore, a simple regression equation for predicting DX

Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.192.5099
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