APPLICATION OF THE PARAMETRIC BOOTSTRAP TO MODELS THAT INCORPORATE A SINGULAR-VALUE DECOMPOSITION

Simulation is a standard technique for investigating the sampling distribution of parameter estimators. The bootstrap is a distribution-free method of assessing sampling variability based on resampling from the empirical distribution; the parametric bootstrap resamples from a fitted parametric model. However, if the parameters of the model are constrained, and the application of these constraints is a function of the realized sample, then the resampling distribution obtained from the parametric bootstrap may become badly biased and overdispersed. Here we discuss such problems in the context of estimating parameters from a bilinear model that incorporates the singular value decomposition (SVD) and in which the parameters are identified by the standard orthogonality relationships of the SVD. Possible effects of the SVD parameter identification are arbitrary changes in the sign of singular vectors, inversion of the order of singular values and rotation of the plotted co-ordinates. This paper proposes inverse transformation or 'filtering' techniques to avoid these problems. The ideas are illustrated by assessing the variability of the location of points in a principal ed-ordinates diagram and in the marginal sampling distribution of singular values. An application to the analysis of a biological data set is described. In the discussion it is pointed out that several exploratory multivariate methods may benefit by using resampling with filtering.