Abstract
This paper will be presented regression models to estimate the assumed income that is of utmost importance to the credit market since the client does not prove your income. The proposed models are lognormal and gamma which is a generalized linear model, both will be compared who perform better will be chosen, where the result of the chosen model will undergo an addition of a noise to get a better estimated. Every analysis and simulation were implemented in [7].
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References
Balintfy, J.L., Goodman, S.S.: Socio-Economic Factors in Income Inequality: A Log-Normal Hypothesis. Zeitschrift für Nationallökonomie, pp. 389–402. Springer (1973)
Buse, A.: The likelihood ratio, Wald and Lagrange multiplier tests: an expository note. The American Statistician 36, 153–157 (1982)
Conover, W.J.: Pratical nonparametric statistics, 3rd edn., pp. 428–433. Moopark, John Wiley & Sons, Inc., New York(1999)
Seber, G.A.F., Lee, A.J.: Linear Regression Analysis, 2nd edn., pp. 99–100. Wiley (2003)
Kohavi, R.: A study of croos validation and boostrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp. 1137–1143. Morgan Kaufmann Publishers (1995)
Ripley, B.D.: Pattern Recognition and neural networks, 1st edn. Cambridge University Press, Cambrigde (1996)
R Development Core Team, http://www.r-project.org/
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Silva Júnior, V.E., Souza, R.M.C.R., Amaral, G.J.A., Souza Júnior, H.G. (2013). Estimation Methods of Presumed Income. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_30
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DOI: https://doi.org/10.1007/978-3-642-42042-9_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42041-2
Online ISBN: 978-3-642-42042-9
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