Premium
Reference curve estimation via alternating sliced inverse regression
Author(s) -
Gannoun Ali,
Guinot Christiane,
Saracco Jérôme
Publication year - 2004
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.630
Subject(s) - sliced inverse regression , covariate , quantile , mathematics , inverse , dimension (graph theory) , statistics , estimation , quantile regression , sufficient dimension reduction , dimensionality reduction , kernel (algebra) , data set , parametric statistics , econometrics , regression , computer science , artificial intelligence , combinatorics , geometry , management , economics
In order to obtain reference curves for data sets when the variable of interest and the covariate are multidimensional, we propose a new methodology in this article based on a dimension‐reduction approach and non‐parametric estimation of conditional quantiles. This approach is semiparametric and combines Alternating Sliced Inverse Regression and a kernel estimation of conditional quantiles. The usefulness of this estimation procedure is illustrated with a real data set collected for the purpose of establishing reference curves for the biophysical properties of the skin of healthy French women. Copyright © 2003 John Wiley & Sons, Ltd.