  This paper deals with a general class of transformation models that contains
many important semiparametric regression models as special cases. It develops a
self-induced smoothing for the maximum rank correlation estimator, resulting in
simultaneous point and variance estimation. The self-induced smoothing does not
require bandwidth selection, yet provides the right amount of smoothness so
that the estimator is asymptotically normal with mean zero (unbiased) and
variance-covariance matrix consistently estimated by the usual sandwich-type
estimator. An iterative algorithm is given for the variance estimation and
shown to numerically converge to a consistent limiting variance estimator. The
approach is applied to a data set involving survival times of primary biliary
cirrhosis patients. Simulations results are reported, showing that the new
method performs well under a variety of scenarios.
