Abstrakt
For some variants of regression models, including partial, measurement error or error-in-variables, latent e®ects, semi-parametric and otherwise corrupted linear models, the classical parametric tests generally do not perform well. Various modifications and generalizationsconsidered extensively in the literature rests on stringent regularity assumptions which are not likely to be tenable in many applications.
However, in such non-standard cases, rank based tests can be adapted better, and further, incorporation of rank analysis of covariance tools enhance their power-efficiency. Numerical studies and a real data illus- tration show the superiority of rank based inference in such corrupted linear models.