Small-Sample condence interval for the slope of linear structural relationship model

Abstract

The asymptotic condence interval of the slope in linear structural rela-tionship model is usually used to draw the inference about parameter. It isnow evident that, asymptotic inference is often unreliable for small-sample.In small samples, asymptotic inference may be unreliable as standard errorsmay be imprecise, leading to incorrect condence intervals and statistical testsize. In these issues, bootstrap can be used instead of asymptotic inference todeal with these challenging problems. We consider both the parametric andthe jackknife-after-bootstrap methods for this particular study. The perfor-mances of both condence intervals are studied by real world data and MonteCarlo simulations. Our ndings show that overall the bootstrap condenceintervals perform better than the asymptotic condence interval for smallsamples in terms of coverage probability with reasonable expected length.