Abstract
Regresyon eğrileri parametrik olmayan bir regresyon analizi türüdür. Regresyon çözümlemesine farklı bir yaklaşım getiren bu yöntem, ekonomi, finans, tıp ve politik bilimlerde sıkça kullanılmaktadır. Bu çalışmada; Devlet Meteoroloji İşleri Genel Müdürlüğünden alınan İzmir iline ait 34 yıllık sıcaklık verileri, düğüm noktalarının konumlarının bilinmesi ve bilinmemesi durumları için ayrı ayrı incelenmiştir. Her bir veri kümesine regresyon eğrileri uygulanarak modellenmiştir.
References
- Baccini, M., Biggeri, A., Lagazi, C., Lertxundi, A., Saez, M., 2007. Parametric and Semi-Parametric Approaches in The Analysis of Short-Term Effects of Air Pollution on Health. Computational Statistics & Data Analysis, 51, 4324 - 4336.
- Eubank, R. L., 1999. Non-Parametric Regression and Spline Smoothing. 2nd ed., Marcel Dekker, USA.
- Hastie, T., Tibshirani, R., Friedman, J., 2008. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag, USA.
- Hines, W. W., Montgomery, D. C., 1990. Probability and Statistics in Engineering and Management Science. John Wiley & Sons, Singapore.
- Lee, T. C. M., 2002. On Algorithms For Ordinary Least Squares Regression Spline Fitting: A Comparative Study. Journal of Statistical Computation and Simulation, 72(8), 647-663.
- Marsh, L.C., 1983. On Estimating Spline Regressions, Proceedings of the Eighth Annual SAS Users Group International Conference. SAS Institute, 723-728.
- Marsh, L. C., 1987. Estimating Spline Knots in Time Series Polynomial Regression Models. The Institute of Management Sciences and Operations Research Society of America, St. Louis, 1-15.
- Marsh, L. C., Cormier D. R., 2002. Spline Regression Models. Sage Publications, USA.
Abstract
Spline regression is a type of non-parametric regression analysis which is frequently applied in economy, finance, medicine and political sciences. In this study, 34 years of temperature data taken from Turkish State Meteorological Service for Izmir is examined under the condition that the locations of knot points are known and unknown. Spline regression method is applied for analyzing each of the data sets.
References
- Baccini, M., Biggeri, A., Lagazi, C., Lertxundi, A., Saez, M., 2007. Parametric and Semi-Parametric Approaches in The Analysis of Short-Term Effects of Air Pollution on Health. Computational Statistics & Data Analysis, 51, 4324 - 4336.
- Eubank, R. L., 1999. Non-Parametric Regression and Spline Smoothing. 2nd ed., Marcel Dekker, USA.
- Hastie, T., Tibshirani, R., Friedman, J., 2008. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag, USA.
- Hines, W. W., Montgomery, D. C., 1990. Probability and Statistics in Engineering and Management Science. John Wiley & Sons, Singapore.
- Lee, T. C. M., 2002. On Algorithms For Ordinary Least Squares Regression Spline Fitting: A Comparative Study. Journal of Statistical Computation and Simulation, 72(8), 647-663.
- Marsh, L.C., 1983. On Estimating Spline Regressions, Proceedings of the Eighth Annual SAS Users Group International Conference. SAS Institute, 723-728.
- Marsh, L. C., 1987. Estimating Spline Knots in Time Series Polynomial Regression Models. The Institute of Management Sciences and Operations Research Society of America, St. Louis, 1-15.
- Marsh, L. C., Cormier D. R., 2002. Spline Regression Models. Sage Publications, USA.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Computational Statistics |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | October 17, 2011 |
| Published in Issue | Year 2011 Volume: 8 Issue: 2 |
