PL EN


2014 | 203 | 154-162
Article title

O interpretacji nieparametrycznych modeli regresyjnych

Authors
Content
Title variants
EN
Parametric Interpretation of Non-Parametric Regression Models
Languages of publication
PL
Abstracts
EN
The advantage of the parametric regression models is the possibility of interpretation of the parameters of the regression model, i.e. to determine the direction and strength of the influence of predictors on the dependent variable. Unfortunately, in practice - the nonlinearity of the real processes, the influence of the phenomena with various probability distributions and a small number of observations limits the building of parametric models while the interpretation of non-parametric models is either impossible or very limited. Frequently such interpretation is useful in the specified range of variation. This may be a typical range of variation - for example, between the second and third quartiles, or a specific range due to the nature of the modeled phenomenon or process. It is difficult however, to build parametric models based only on the range of explanatory variables, because in this way we exclude observations giving additional knowledge into the model. The essence of this study is to enable the interpretation of non-parametric models through the creation of additional observations with these models in an interesting range of explanatory variables. These observations create secondary dataset used for the construction of a parametric model, which can now be interpreted. Presented investigations compare - using simulation - parametric models created for secondary sample with parametric models calculated for the original data.
Year
Volume
203
Pages
154-162
Physical description
Contributors
References
  • Drucker C.J., Burges C.J.C., Kaufman L., Smola A., Vapnik V. (1997): Support Vector Regression Machines. "Advances in Neural Information Processing Systems", Vol. 9.
  • Friedman J. (1991): Multivariate Adaptive Regression Splines. "Annals of Statistics", Vol. 19, Institute of Mathematical Statistics, Stanford University.
  • Gatnar A. (2001): Nieparametryczna metoda dyskryminacji i regresji. Wydawnictwo Naukowe PWN, Warszawa.
  • Gatnar E. (2008): Podejście wielomodelowe w zagadnieniach dyskryminacji i regresji. Wydawnictwo Naukowe PWN, Warszawa.
  • Maddala G.S. (2008): Ekonometria. Wydawnictwo Naukowe PWN, Warszawa.
  • Statystyczna analiza danych z wykorzystaniem programu R (2009). Red. M. Walesiak, E. Gatnar. Wydawnictwo Naukowe PWN, Warszawa.
  • Statystyczne metody analizy danych (1998). Red. W. Ostasiewicz. Wydawnictwo AE, Wrocław.
  • Tadeusiewicz R., Lula P. (2000): Neuronowe metody analizy szeregów czasowych i możliwości ich zastosowań w zagadnieniach biomedycznych. W: Biocybernetyka i inżynieria biomedyczna. Tom 6. Sieci neuronowe. Red. M. Nałęcz. Akademicka Oficyna Wydawnicza Exit, Warszawa.
Document Type
Publication order reference
Identifiers
ISSN
2083-8611
YADDA identifier
bwmeta1.element.desklight-a9669429-e8ec-4a06-848a-f43a87f3b536
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.