  For some variants of regression models, including partial, measurement error
or error-in-variables, latent effects, semi-parametric and otherwise corrupted
linear models, the classical parametric tests generally do not perform well.
Various modifications and generalizations considered 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 illustration show the superiority of rank based inference in such
corrupted linear models.
