Biblioteca de la Universidad Complutense de Madrid

A flexible Tool for Model Building: the Relevant Transformation of the Inputs Network Approach

Impacto



Pérez Amaral, Teodosio y Gallo, Giampiero M. y White, Halbert (2002) A flexible Tool for Model Building: the Relevant Transformation of the Inputs Network Approach. [ Documentos de trabajo del Instituto Complutense de Análisis Económico (ICAE); nº 01, 2002, ]

[img]
Vista previa
PDF
117kB

URL Oficial: http://eprints.ucm.es/7650/




Resumen

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.


Tipo de documento:Documento de trabajo o Informe técnico
Palabras clave:RETINA
Materias:Ciencias Sociales > Economía > Comercio
Título de serie o colección:Documentos de trabajo del Instituto Complutense de Análisis Económico (ICAE)
Volumen:2002
Número:01
Código ID:7650
Referencias:

Akaike, H. (1973) “Information Theory and an Extension of the Likelihood Principle”, in B. N. Petrov and F. Csaki (eds.), Proceedings of the Second International Symposium on Information Theory. Budapest: Akademiai Kiado, 267-281.

Breiman, L. (1992) “The Little Bootstrap and Other Methods for Dimensionality Selection in Regression: X-fixed Prediction Error,” Journal of the American Statistical Association, Vol. 87, No. 419, 738-754.

Breiman, L. (1995) “Better Subset Regression Using the Nonnegative Garrote” Technometrics, Vol. 37, 4, 373-384.

Breiman, L. and H. Friedman (1985) “Estimating Optimal Transformations for Multiple Regression and Correlation” Journal of the American Statistical Association, Vol. 80, No. 391, 580-619.

Breiman, L., Friedman, H., Olshen, R. and C. Stone (1984) Classification and Regression Trees, Wadsworh Statistics/Probability Series, Belmont, California.

Burnham, K. and D. Anderson (1998) Model Selection and Inference: A Practical Information-Theoretic Approach, Springer-Verlag, New York.

Chipman, H., E. George and R. McCulloch (1998) “Bayesian CART Model Search”, Journal of the American Statistical Association, Vol. 93, 935-960.

Diebold, Francis X., and Roberto S. Mariano, Comparing Predictive Accuracy, Journal of Business and Economic Statistics, v.13, no.3 (July 1995), pp. 253-63.

Granger, C.W.J., M. King and H. White, (1995) , Comments on Testing Economic Theories and the Use of Model Selection Criteria, Journal of Econometrics, 67, 173-187.

Hastie, T. J. and R. J. Tibshirani (1990) Generalized Additive Models, Monographs on Statistics and Applied Probability 43, Chapman and Hall, London.

Hoover, K. and J. Perez (1999) “Data Mining Reconsidered: Encompassing and the General-to-Specific Approach to Specification Search”, manuscript, Department of Economics UC Davis.

Miller, A. J. (1990) Subset Selection in Regression, Monographs on Statistics and Applied Probability 40, Chapman and Hall, London.

Shao, J. (1993) “Linear Model Selection by Cross-Validation”, Journal of the American Statistical Association, Vol. 88, No. 422, 486-494.

Shao, J. (1996) “Bootstrap Model Selection”, Journal of the American Statistical Association, Vol. 91, No. 434, 655-665.

White, H., (1989), Learning in artificial neural networks: a statistical perspective, Neural Computation, 1, pp. 425-464.

White, H. (1998) Artificial Neural Network and Alternative Methods for Assessing Naval Readiness. Technical Report, NRDA, San Diego.

White, H. (2000), “A Reality Check for Data Snooping,” Econometric a, 68, 1097—1126.

West, K. D. (1996). Asymptotic inference about predictive ability. Econometrica 64, 1067–84.

Zhang, P. (1992) “On the Distributional Properties of Model Selection Criteria”, Journal of the American Statistical Association, Vol. 87, No. 419, 732-737.

Depositado:26 Feb 2008
Última Modificación:06 Feb 2014 07:55

Sólo personal del repositorio: página de control del artículo