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Otoregresif Modellerin Bayes Analizinin Hava Kirliliği Verilerine Uygulaması

Year 2005, Volume: 4 Issue: 1, 1 - 11, 15.04.2005

Abstract

Otoregresif (AR) modellerin istatistiksel analizi önemli bir çıkarım problemidir. Çoğu klasik yaklaşımlar, AR modelinin derecesinin belirlenmesinde ve parametrelerin tahmininde geniş olarak kullanılır. Bayesci yaklaşım da her iki amaç içinde kullanılabilir. Bu çalışmada AR modelinin derecesinin belirlenmesinde klasik yaklaşımlar kullanıldı ve Samsun bölgesindeki hava kirliliği verisi için gelecek değerleri tahmin etmede bilgi içermeyen önsellerin kullanımıyla Bayesci yaklaşım incelendi.

References

  • BOX, G.E.P. and JENKINS G.M. (1976), Time Series Analysis, Forecasting and Control, Holden Day.
  • BROEMLING, L. D. and SHAARAWAY, S., (1988), Time Series: A Bayesian Analysis in the Time Domain. In Bayesian Analysis of Time Series and Dynamic Models, Dekker, New York.
  • CHIB, S. and GREENBERG, E. (1994), Bayes Inference in Regressions Models with ARMA (p,q) Errors, Journal of Econometrics, 64,183-206.
  • MARRIOTT, J. M. and SMITH, A.F.M. (1992), Reparametrization Aspects of Numerical Bayesian Methods for Autoregressive Moving Avarage Models, Journal of Time Series Analysis, 13, 327-343.
  • MCCULLOCH, R.E. and TSAY, R. S. (1994), Bayesian Analysis of Autoregressive Time Series Via the Gibbs Sampler, Journal of Time Series Analysis, 15, 235-250.
  • MONAHAN, J.F. (1983), Fully Bayesian Analysis of ARMA Time Series Models, Journal of Econometrics, 21, 307-331.
  • ZELLNER, A. (1971), an Introduction to Bayesian Inference in Econometrics, John-Wiley&Sons, Inc.

Bayesian Analysis of Autoregresive Models With an Application to Air Pollution Data

Year 2005, Volume: 4 Issue: 1, 1 - 11, 15.04.2005

Abstract

Statistical analysis of autoregressive (AR) models is an important inference problem. Most statistical approaches are widely used in determining the order of the AR model and prediction the parameters. The Bayesian approach can be used for both aim as well. In this study we use the classical approach in determining the order of the AR model and investigate the Bayesian approach with different non-informative priors in predicting future values of the air pollution data for Samsun region.

References

  • BOX, G.E.P. and JENKINS G.M. (1976), Time Series Analysis, Forecasting and Control, Holden Day.
  • BROEMLING, L. D. and SHAARAWAY, S., (1988), Time Series: A Bayesian Analysis in the Time Domain. In Bayesian Analysis of Time Series and Dynamic Models, Dekker, New York.
  • CHIB, S. and GREENBERG, E. (1994), Bayes Inference in Regressions Models with ARMA (p,q) Errors, Journal of Econometrics, 64,183-206.
  • MARRIOTT, J. M. and SMITH, A.F.M. (1992), Reparametrization Aspects of Numerical Bayesian Methods for Autoregressive Moving Avarage Models, Journal of Time Series Analysis, 13, 327-343.
  • MCCULLOCH, R.E. and TSAY, R. S. (1994), Bayesian Analysis of Autoregressive Time Series Via the Gibbs Sampler, Journal of Time Series Analysis, 15, 235-250.
  • MONAHAN, J.F. (1983), Fully Bayesian Analysis of ARMA Time Series Models, Journal of Econometrics, 21, 307-331.
  • ZELLNER, A. (1971), an Introduction to Bayesian Inference in Econometrics, John-Wiley&Sons, Inc.
There are 7 citations in total.

Details

Primary Language Turkish
Subjects Statistics
Journal Section Research Articles
Authors

Mehmet Ali Cengiz

Erol Eğrioğlu This is me

Publication Date April 15, 2005
Published in Issue Year 2005 Volume: 4 Issue: 1

Cite

APA Cengiz, M. A., & Eğrioğlu, E. (2005). Otoregresif Modellerin Bayes Analizinin Hava Kirliliği Verilerine Uygulaması. İstatistik Araştırma Dergisi, 4(1), 1-11.