Template-type: ReDIF-Paper 1.0
Author-Name: Halbert L. White 
Author-Email: hwhite@weber.ucsd.edu
Author-Workplace-Name: Department of Economics, University of California, San Diego,
Author-Name: Giampiero M. Gallo
Author-Email: gallog@ds.unifi.it
Author-Workplace-Name: Dipartimento di Statistica "G.Parenti", University of Florence, Italy
Author-Name: Teodosio Pérez Amaral
Author-Email: teodosio@ccee.ucm.es
Author-Workplace-Name: Universidad Complutense de Madrid,  Departamento de Analisis Economico
Title:  A flexible Tool for Model Building: the Relevant Transformation of the Inputs Network Approach (RETINA)
Abstract: A new method, called relevant transformation of the inputs network approach (RETINA) is proposed as a 
	tool for model building and selection. It is designed to improve some of the shortcomings of neural 
	networks. It has the flexibility of neural network models, the concavity of the likelihood in the 
	weights of the usual likelihood models, and the ability to identify a parsimonious set of attributes 
	that are likely to be relevant for predicting out of sample outcomes. RETINA expands the range of models 
	by considering transformations of the original inputs; splits the sample in three disjoint subsamples, 
	sorts the candidate regressors by a saliency feature, chooses the models in subsample 1, uses subsample 
	2 for parameter estimation and subsample 3 for cross-validation. It is modular, can be used as a data 
	exploratory tool and is computationally feasible in personal computers.
	In tests on simulated data, it achieves high rates of successes when the sample size or the R2 are 
	large enough. As our experiments show, it is superior to alternative procedures such as the non negative 
	garrote and forward and backward stepwise regression.
Creation-Date: 2002
X-File-Ref: http://america.sim.ucm.es/repec/ucm/ref/doicae0201.txt
File-URL: https://eprints.ucm.es/id/eprint/7650/1/0201.pdf
File-Format: Application/pdf
Handle: RePEc:ucm:doicae:0201