Wykorzystanie regresji nieparametrycznej do modelowania wielkości oszczędności gospodarstw domowych

• Authors: Joanna Trzęsiok

Title variants

EN

Nonparametric Regression Applied to Modelling Household Savings

• Languages of publication: PL

Abstracts

EN

In the paper the procedure for selecting the best nonparametric model for a given problem of regression is presented. This procedure has two stages. In the first one, many nonparametric models of regression, for different parameters settings, are built. Then the model with the smallest mean squared error is chosen. In the second stage, the method for the reduction of insignificant predictors is used. This procedure is applied to modelling household savings.

Keywords

PL

Badania empiryczne; Oszczędności gospodarstw domowych; Regresja nieparametryczna

EN

Empirical researches; Household savings; Nonparametric regression

• Publisher: Uniwersytet Ekonomiczny w Katowicach
• Journal: Studia Ekonomiczne
• Year: 2013
• Volume: 159
• Pages: 99-108

Contributors

author

Joanna Trzęsiok

References

• Bishop C. (1995): Neural Networks for Pattern Recognition. Oxford University Press, Oxford.
• Breiman L. (1996): Bagging Predictors. "Machine Learning", 24, s. 123-140.
• Breiman L. (2001): Random Forests. "Machine Learning", 45, s. 5-32.
• Breiman L., Friedman J.H. (1985): Estimating Optimal Transformations for Multiple Regression and Correlation (with discussion). "Journal of the American Statistical Association", 80, s. 580-619.
• Breiman L., Friedman J.H., Olshen R.A., Stone C.J. (1984): Classification and Regression Trees. Chapman & Hall, New York.
• Friedman J. (1999a): Greedy Function Approximation: A Gradient Boosting Machine. Technical Report. Department of Statistics, Stanford University, Redwood City, CA.
• Friedman J. (1999b): Stochastic Gradient Boosting. Technical Report. Stanford University, Dept. of Statistics.
• Friedman J., Stuetzle W. (1981): Projection Pursuit Regression. "Journal of the American Statistical Association", 76, s. 817-823.
• Gatnar E. (2001): Nieparametryczna metoda dyskryminacji i regresji. "Biblioteka ekonometryczna", Wydawnictwo Naukowe PWN, Warszawa.
• GUS (2001): Warunki życia ludności w 2000 r. Warszawa.
• Hastie T., Tibshirani R., Friedman J. (2001): The Elements of Statistical Learning: Data Mining, Inference and Prediction. "Springer Series in Statistics", Springer Verlag, New York.
• Kooperberg C., Bose S., Stone C. (1997): Polychotomous Regression. "Journal of the American Statistical Association", 92, s. 117-127.
• Meyer D., Leisch F., Hornik K. (2003): The Support Vector Machine under Test. "Neurocomputing", 55(1-2), s. 169-186.
• Nagatani T., Abe S. (2007): Backward Variable Selection of Support Vector Regressors by Block Deletion. Proceedings of the International Joint Conference on Neural Networks, IJCNN 2007, IEEE, s. 2117-2122.
• Trzęsiok J. (2010): Dobór zmiennych do modelu regresyjnego zbudowanego za pomocą wybranych nieparametrycznych metod regresji. W: Klasyfikacja i analiza danych - teoria i zastosowania. Red. K. Jajuga, M. Walesiak. Taksonomia, 17, Wydawnictwo Uniwersytetu Ekonomicznego, Wrocław, s. 172-180.
• Vapnik V. (1998): Statistical Learning Theory. Adaptive and Learning Systems for Signal Processing, Communications, and Control. John Wiley & Sons, New York.

Identifiers

ISSN

2083-8611