Generalized Signed-Rank Estimation for Nonlinear Models

Asheber Abebe
Discrete and Statistical Sciences
Auburn University, AL 36849


We study conditions sufficient for strong consistency of a class
of estimators of parameters of nonlinear regression models. The
study considers continuous functions depending on a vector of
parameters and a set of regressors. The estimators chosen are
minimizers of a generalized form of the signed-rank norm. The
generalization allows us to make asymptotic statements about
minimizers of a wide variety of norms including the $L_1$ and
$L_2$ norms.