Regression NSS: An alternative to cross validation

Abstract

The Noise Sensitivity Signature (NSS), originally introduced by Grossman and Lapedes (1993), was proposed as an alternative to cross validation for selecting network complexity. In this paper, we extend NSS to the general problem of regression estimation. We also present results from regularized linear regression simulations which indicate that for problems with few data points, NSS regression estimates perform better than Generalized Cross Validation (GCV) regression estimates [7].