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Abstrakty
This article is devoted to the comparison between two alternative approaches to the economic models creation and to the estimation of their parameters. The first approach is to create a multiple regression model whereas the second one is to use neural modeling. Both methods are applied to estimation of an exemplary econometric model.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
65--81
Opis fizyczny
Bibliogr. 21 poz., rys.
Twórcy
autor
- Institute of Computer Science, Jagiellonian University, Nawojki 11, 30-072 Cracow, Poland
autor
- Institute of Computer Science, Jagiellonian University, Nawojki 11, 30-072 Cracow, Poland
autor
- Department of Statistics and Computer Science, AWF, Kraków
Bibliografia
- [1] Coats P.K., Fant F; Recognizing financial distress patterns using a neural net¬work tool, Financial Management, 22, 1993, pp. 142-156.
- [2] Bielecki A.; Dynamical properties of learning process of weakly linear and nonlinear neurons (Simulation of a nonlinear econometric model using neuran networks), Nonlinear Analysis: Real World Applications, 2, 2001, pp. 249-258.
- [3]Bielecki A., Bielecka M., Podolak I.T.; Symulacja pewnego nieliniowego modelu ekonometrycznego przy pomocy sieci neuronowych, Przegląd Statystyczny, 48,1- 2, 2001 (in Polish), pp. 151-159.
- [4]Cherkassky V., Lari-Najafi H.; Constraint topological mapping for nonparametric regression analysis, Neural Networks, 4,1, 1991, pp. 27-40.
- [5]Cybenko G.; Approximation by superposition of a sigmoidal function, Mathematics of Control, Signals, and Systems, 2, 1989, pp. 303-314.
- [G] Hecht-Nielsen R.; Kolmogorov’s mapping neural network existence theorem, Proc. of Int. Conf. on Neural Networks, part. III, IEEE Press, New York, 1987.
- [7]Hornik K.; Approximation capabilities of multilayer feed-forward networks, Neural Networks, 4,2, 1991, pp. 251-258.
- [8]Hertz J., Krogh A., Palmer R.G.; Introduction to the Theory of Neural Computation, Addison-Wesley Publishing Company, Massachusetts, 1991.
- [9]Jóźwiak J., Podgórski J.; Statystyka od podstaw, (Foundations of Statistics), PWE, Warszawa 1997 (in Polish).
- [10]Kryzanowski L., Galler M., Wright D.; Using artificial neural networks to pick stocks. Financial Analysts Journal, 49, 1993, pp. 21-27.
- [11]Klapper R.; SpiderWeb http://www.cs.nyu.edu/~klap7794/spiderweb2.html
- [12]Kolmogorov A.N.; On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition, Doki. Akad. Nauk ZSRR. 114, 1957, pp. 953-956.
- [13]Kondo T.; Short-term prediction of air pollution concentration by a neural network, Transactions of the Society of Instrument and Control Engineers, 3, 1993, pp. 710-718.
- [14]Kurkova V.; Kolmogorov’s theorem and multilayer neural networks, Neural Networks, 5,3, 1992, pp. 501-506.
- [15]Lula P.; Próba zastosowania algorytmów genetycznych do szacowania parametrów funkcji regresji (A genetic approach to estimate regression function parameters), Zeszyty Naukowe Akademii Ekonomicznej w Krakowie, 474, 1996 (in Polish), pp. 63-73.
- [16]Müller B., Reinhardt J.; Neural Networks, Springer Verlag, New York 1990.
- [17]Nowak E.; Propozycje wyznaczania parametrów pewnego nieliniowego modelu ekonometrycznego (Proposals for a nonlinear econometric model parameters estimation), Przegląd Statystyczny, 36, 1989 (in Polish), pp. 31-39.
- [18]Podolak I.T.; Feedforward neural network’s sensitivity to input data representation, Computer Physics Communications, 117, 1999, pp. 181-188.
- [19]Trippi R.R., DeSieno D.; Trading equity index futures with a neural network, Journal of Portfolio Management, Fall 1992, 1992, pp. 27-33.
- [20]Venugopal V., Beats W.; Neural networks and statistical techniques in marketing research: a conceptual comparison, Marketing Intelligence k Planning, 12,7, 1994, pp. 30-38.
- [21]Żurada J.; Introduction to Artificial Systems, West, Publishing Co., St.. Paul, USA, 1992.
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Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUJ1-0019-0102