Finanzas, fraude
Páginas: 27 (6697 palabras)
Publicado: 10 de mayo de 2010
Bibliografía
Bibliografía
411
Bibliografía
1.
Aapo Hyvärinen; Ella Bingham.(2003).Connection between multilayer perceptrons and regression using independent component analysis, Neurocomputing, Vol. 50, pp. 211-222.
2.
Abdi, Hervé. (1994). Les réseaux de neurones, Presses universitaires de Grenoble, Grenoble.
3.
Aiken, M.; Krosp, J.; Vanjani, M.; Govindarajulu, Ch.;Sexton, R. (1994). A Neural Network for Predicting Total Industrial Production, Journal of End User Computing, Vol. 7, No. 2, pp. 19-23.
4.
Albizuri Irigoyen, F.J. (1995). Máquina de Boltzmann de alto orden: una red neuronal con técnicas de Monte Carlo para el modelado de Distribuciones de Probabilidad. Caracterización y Estructura, Tesis Doctoral, Universidad del País Vasco.
5.
AlcañizZanón, M. (1996). Modelos de Poisson generalizados con una variable de exposición al riesgo, Tesis Doctoral, Universidad de Barcelona, Barcelona.
6.
Alon, I.; Min Qi; Sadowski, R.J. (2001). Forecasting agregate retail sales: a comparison of artificial neural networks and traditional methods, Journal of Retailing and Consumer Services, 8, pp.147-156.
7.
Altman, E.I. (1992). CorporateFinancial Distress and Bankruptcy. A complete Guide to Predicting & Avoiding Distress and Profiting from Bankruptcy, 2ª Ed., Wiley.
8.
Arques, P.A.(1997). La predicción del Fracaso Empresarial. Aplicación al Riesgo Crediticio Bancario, Tesis Doctoral, Univesidad de Murcia.
412
Bibliografía
9.
Artís, M; Clar, M; Barrio, T.; Guillén, M; Suriñach, J. (2000). Tòpics d’ econometria,Ediuoc, Barcelona.
10.
Artis, M; Suriñach, J.; López, E.; Sansó, A. (1995). Análisis Económico Regional. Nociones básicas de la Teoría de la Cointegración, Bosch, Barcelona.
11.
Ayuso Gutiérrez, M. (1998). Modelos Econométricos para la detección del fraude en el seguro del automóvil, Tesis Doctoral, Univesidad de Barcelona, Barcelona.
12.
Azoff, E.M. (1994). Neural Netwoks TimeSeries forecasting of Financial Markets, Wiley.
13.
Back, B.; Toivonen, J. Vanharanta, H. Visa, A.(2001). Comparing numerical data and text information form annual reports using self-organizing maps, International Journal of Accounting Information Systems, 2, pp. 249-269.
14.
Baestaens, D.E.; Van Den Bergh, W.M.; Wood, D. (1994). Neural Network Solutions for trading in Financial Markets,Pitman Publishing.
15.
Bakshi, B.R. Utojo, U.(1998). Unification of neural and statistical modeling methods that combine inputs by linear projection, Computers Chem. Engng, Vol. 22, No.12, pp.1859-1878.
16.
Baranoff, E.G; Sager, T.W.; Shively, T.S. (2000). A Semiparametric Stochastic Spline Model as a Managerial Tool for Potencial Insolvency, The Journal of Risk and Insurance, Vol. 67,No. 3, pp. 369-396.
17.
Barndorff-Nielsen, O.E.; Jensen, J.L.; Kendall, W.S. (1993). Networks and Chaos – Statistical and Probabilistic Aspects, Chapman & Hall.
18.
Barnes, P. (1982). Methodological implications of Non-normaly Distributed Financial Ratios, Journal of Business, Finance & Accounting,Vol. 9, No 1, pp. 51-62.
Bibliografía
413
19.
Barnes, P. (1983).Methodological implications of Non-normaly Distributed Financial Ratios: A Reply, Journal of Business, Finance & Accounting, Vol. 10, No 4, pp. 691-693.
20.
Barnes, P. (1987). The Analysis and Use of Financial Ratios: A Review Article, Journal of Business, Finance & Accounting, Vol. 14, No. 4, pp. 691-693.
21.
BarNiv, R.; McDonald, J.B. (1992). Identifying Financial Distress in the InsuranceIndustry: A Sintesis of Methodological and Empirical Issues, The Journal of Risk and Insurance, Vol. LIX, No. 4, pp. 543-574.
22.
Barreto F. (1998). Genetic algorithms applications in the analysis of insolvency risk, Journal of Banking & Finance, 22, pp. 1421-1439.
23.
Baum, E. (1990).When are k-nearest neighbor and back propagation accurate for feasible sized sets of examples?, Lecture...
Bibliografía
411
Bibliografía
1.
Aapo Hyvärinen; Ella Bingham.(2003).Connection between multilayer perceptrons and regression using independent component analysis, Neurocomputing, Vol. 50, pp. 211-222.
2.
Abdi, Hervé. (1994). Les réseaux de neurones, Presses universitaires de Grenoble, Grenoble.
3.
Aiken, M.; Krosp, J.; Vanjani, M.; Govindarajulu, Ch.;Sexton, R. (1994). A Neural Network for Predicting Total Industrial Production, Journal of End User Computing, Vol. 7, No. 2, pp. 19-23.
4.
Albizuri Irigoyen, F.J. (1995). Máquina de Boltzmann de alto orden: una red neuronal con técnicas de Monte Carlo para el modelado de Distribuciones de Probabilidad. Caracterización y Estructura, Tesis Doctoral, Universidad del País Vasco.
5.
AlcañizZanón, M. (1996). Modelos de Poisson generalizados con una variable de exposición al riesgo, Tesis Doctoral, Universidad de Barcelona, Barcelona.
6.
Alon, I.; Min Qi; Sadowski, R.J. (2001). Forecasting agregate retail sales: a comparison of artificial neural networks and traditional methods, Journal of Retailing and Consumer Services, 8, pp.147-156.
7.
Altman, E.I. (1992). CorporateFinancial Distress and Bankruptcy. A complete Guide to Predicting & Avoiding Distress and Profiting from Bankruptcy, 2ª Ed., Wiley.
8.
Arques, P.A.(1997). La predicción del Fracaso Empresarial. Aplicación al Riesgo Crediticio Bancario, Tesis Doctoral, Univesidad de Murcia.
412
Bibliografía
9.
Artís, M; Clar, M; Barrio, T.; Guillén, M; Suriñach, J. (2000). Tòpics d’ econometria,Ediuoc, Barcelona.
10.
Artis, M; Suriñach, J.; López, E.; Sansó, A. (1995). Análisis Económico Regional. Nociones básicas de la Teoría de la Cointegración, Bosch, Barcelona.
11.
Ayuso Gutiérrez, M. (1998). Modelos Econométricos para la detección del fraude en el seguro del automóvil, Tesis Doctoral, Univesidad de Barcelona, Barcelona.
12.
Azoff, E.M. (1994). Neural Netwoks TimeSeries forecasting of Financial Markets, Wiley.
13.
Back, B.; Toivonen, J. Vanharanta, H. Visa, A.(2001). Comparing numerical data and text information form annual reports using self-organizing maps, International Journal of Accounting Information Systems, 2, pp. 249-269.
14.
Baestaens, D.E.; Van Den Bergh, W.M.; Wood, D. (1994). Neural Network Solutions for trading in Financial Markets,Pitman Publishing.
15.
Bakshi, B.R. Utojo, U.(1998). Unification of neural and statistical modeling methods that combine inputs by linear projection, Computers Chem. Engng, Vol. 22, No.12, pp.1859-1878.
16.
Baranoff, E.G; Sager, T.W.; Shively, T.S. (2000). A Semiparametric Stochastic Spline Model as a Managerial Tool for Potencial Insolvency, The Journal of Risk and Insurance, Vol. 67,No. 3, pp. 369-396.
17.
Barndorff-Nielsen, O.E.; Jensen, J.L.; Kendall, W.S. (1993). Networks and Chaos – Statistical and Probabilistic Aspects, Chapman & Hall.
18.
Barnes, P. (1982). Methodological implications of Non-normaly Distributed Financial Ratios, Journal of Business, Finance & Accounting,Vol. 9, No 1, pp. 51-62.
Bibliografía
413
19.
Barnes, P. (1983).Methodological implications of Non-normaly Distributed Financial Ratios: A Reply, Journal of Business, Finance & Accounting, Vol. 10, No 4, pp. 691-693.
20.
Barnes, P. (1987). The Analysis and Use of Financial Ratios: A Review Article, Journal of Business, Finance & Accounting, Vol. 14, No. 4, pp. 691-693.
21.
BarNiv, R.; McDonald, J.B. (1992). Identifying Financial Distress in the InsuranceIndustry: A Sintesis of Methodological and Empirical Issues, The Journal of Risk and Insurance, Vol. LIX, No. 4, pp. 543-574.
22.
Barreto F. (1998). Genetic algorithms applications in the analysis of insolvency risk, Journal of Banking & Finance, 22, pp. 1421-1439.
23.
Baum, E. (1990).When are k-nearest neighbor and back propagation accurate for feasible sized sets of examples?, Lecture...
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