title: Robust estimation and inference for bivariate line-fitting in allometry.
abstract: In allometry, bivariate techniques related to principal component analysis are often used in place of linear regression, and primary interest is in making inferences about the slope. We demonstrate that the current inferential methods are not robust to bivariate contamination, and consider four robust alternatives to the current methods -- a novel sandwich estimator approach, using robust covariance matrices derived via an influence function approach, Huber's M-estimator and the fast-and-robust bootstrap. Simulations demonstrate that Huber's M-estimators are highly efficient and robust against bivariate contamination, and when combined with the fast-and-robust bootstrap, we can make accurate inferences even from small samples.
authors.affiliation: Department of Mathematics and Statistics, University of Jyväskylä, FIN-40014 University of Jyväskylä, Finland. sara.l.taskinen@jyu.fi
authors.forename: Sara
authors.initials: S
authors.lastname: Taskinen
authors.forename: David I
authors.initials: DI
authors.lastname: Warton
mesh_terms: D000704:Analysis of Variance; D056808:Biostatistics / Q000379:methods*; D049628:Body Size; D011336:Probability