BibTex RIS Cite

A Bayesian Approach for Parameter Estimation in Logistic Regression

Year 2012, Volume: 12 Issue: 1, 15 - 22, 01.04.2012

Abstract

References

  • Berger, J.O., 1985. Statistical Decision Theory and Bayesian Analysis, New York: Springer- Verlag.
  • Berger, J.O., 2006. The Case for Objective Bayesian Analysis. Bayesian Analysis, 3,385–402.
  • Berger, J.O. and Wolpert, R., 1988. The Likelihood Principle, 9, Second Edition, Hayward, California: Institute of Mathematical Statistics, monograph series.
  • Bernardo, J.M. and Smith, A.F.M., 1994. Bayesian Theory, New York: John Wiley & Sons.
  • Bernardo, J.M. and Smith, A.F.M., 2000. Bayesian Theory, New York: John Wiley & Sons.
  • Carlin, B.P. and Louis, T.A., 2000. Bayes and Empirical Bayes Methods for Data Analysis, Second Edition, London: Chapman & Hall.
  • Chen, M.H., Shao, Q.M. and Ibrahim, J.G., 2000. Monte Carlo Methods in Bayesian Computation, New York: Springer-Verlag.
  • Congdon, P., 2001. Bayesian Statistical Modeling, John Wiley & Sons.
  • Congdon, P., 2003. Applied Bayesian Modeling, John Wiley & Sons.
  • Congdon, P., 2005. Bayesian Models for Categorical Data, John Wiley & Sons.
  • Gelfand, A.E., Hills, S.E., Racine-Poon, A. and Smith, A.F.M., 1990. llustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling. Journal of the American Statistical Association, 85, 972– 985.
  • Gelman, A., Carlin, J., Stern, H. and Rubin, D., 2004. Bayesian Data Analysis, Second Edition, London: Chapman & Hall.
  • Geman, S. and Geman, D., 1984. Stochastic Relaxation, Gibbs Distribution, and the Bayesian Restoration of Images. IEEE Transaction on Pattern Analysis and Machine Intelligence, 6, 721–741.
  • Gilks, W.R., Richardson, S. and Spiegelhalter, D.J., 1996. Markov Chain Monte Carlo in Practice, London: Chapman & Hall.
  • Goldstein, M., 2006. Subjective Bayesian Analysis: Principles and Practice. Bayesian Analysis, 3, 403– 420.
  • Jeffreys, H., 1961. Theory of Probability, third Edition, Oxford: Oxford University Press.
  • Kass, R.E. and Wasserman, L., 1996. Formal Rules of Selecting Prior Distributions: A Review and Annotated Bibliography. Journal of the American Statistical Association, 91, 343–370.
  • Liu, J.S., 2001. Monte Carlo Strategies in Scientific Computing, Springer-Verlag.
  • O’Hagan, A., 1994. Bayesian Inference, volume 2B of Kendall’s Advanced Theory of Statistics, London: Arnold.
  • Robert, C.P., 2001. The Bayesian Choice, Second Edition, New York: Springer-Verlag.
  • Robert, C.P. and Casella, G., 2004. Monte Carlo Statistical Methods, 2nd ed. New York: Springer- Verlag.
  • Tanner, M.A., 1993. Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions, New York: Springer-Verlag.
  • Wasserman, L., 2004. All of Statistics: A Concise Course in Statistical Inference, New York: Springer- Verlag.

Lojistik Regresyonda Parametre Tahmininde Bayesci Bir Yaklaşım

Year 2012, Volume: 12 Issue: 1, 15 - 22, 01.04.2012

Abstract

İstatistikte Bayesci çıkarım, ilave bir bilgi öğrenildiğinde bir parametrenin sonsal tahminini güncellemede Bayes kuralını kullanan bir çıkarım metodudur. Bayesci güncelleme İstatistikte özellikle matematiksel istatistikte önemli bir yöntemdir. Bir istatistiksel metot için Bayesci çıkarım otomatik olarak herhangi bir hesaplama metodu kadar iyi çalışır. Bu çalışmada iki gerçek veri üzerinde Lojistik regresyon için Bayesci yaklaşım verilmiştir.

References

  • Berger, J.O., 1985. Statistical Decision Theory and Bayesian Analysis, New York: Springer- Verlag.
  • Berger, J.O., 2006. The Case for Objective Bayesian Analysis. Bayesian Analysis, 3,385–402.
  • Berger, J.O. and Wolpert, R., 1988. The Likelihood Principle, 9, Second Edition, Hayward, California: Institute of Mathematical Statistics, monograph series.
  • Bernardo, J.M. and Smith, A.F.M., 1994. Bayesian Theory, New York: John Wiley & Sons.
  • Bernardo, J.M. and Smith, A.F.M., 2000. Bayesian Theory, New York: John Wiley & Sons.
  • Carlin, B.P. and Louis, T.A., 2000. Bayes and Empirical Bayes Methods for Data Analysis, Second Edition, London: Chapman & Hall.
  • Chen, M.H., Shao, Q.M. and Ibrahim, J.G., 2000. Monte Carlo Methods in Bayesian Computation, New York: Springer-Verlag.
  • Congdon, P., 2001. Bayesian Statistical Modeling, John Wiley & Sons.
  • Congdon, P., 2003. Applied Bayesian Modeling, John Wiley & Sons.
  • Congdon, P., 2005. Bayesian Models for Categorical Data, John Wiley & Sons.
  • Gelfand, A.E., Hills, S.E., Racine-Poon, A. and Smith, A.F.M., 1990. llustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling. Journal of the American Statistical Association, 85, 972– 985.
  • Gelman, A., Carlin, J., Stern, H. and Rubin, D., 2004. Bayesian Data Analysis, Second Edition, London: Chapman & Hall.
  • Geman, S. and Geman, D., 1984. Stochastic Relaxation, Gibbs Distribution, and the Bayesian Restoration of Images. IEEE Transaction on Pattern Analysis and Machine Intelligence, 6, 721–741.
  • Gilks, W.R., Richardson, S. and Spiegelhalter, D.J., 1996. Markov Chain Monte Carlo in Practice, London: Chapman & Hall.
  • Goldstein, M., 2006. Subjective Bayesian Analysis: Principles and Practice. Bayesian Analysis, 3, 403– 420.
  • Jeffreys, H., 1961. Theory of Probability, third Edition, Oxford: Oxford University Press.
  • Kass, R.E. and Wasserman, L., 1996. Formal Rules of Selecting Prior Distributions: A Review and Annotated Bibliography. Journal of the American Statistical Association, 91, 343–370.
  • Liu, J.S., 2001. Monte Carlo Strategies in Scientific Computing, Springer-Verlag.
  • O’Hagan, A., 1994. Bayesian Inference, volume 2B of Kendall’s Advanced Theory of Statistics, London: Arnold.
  • Robert, C.P., 2001. The Bayesian Choice, Second Edition, New York: Springer-Verlag.
  • Robert, C.P. and Casella, G., 2004. Monte Carlo Statistical Methods, 2nd ed. New York: Springer- Verlag.
  • Tanner, M.A., 1993. Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions, New York: Springer-Verlag.
  • Wasserman, L., 2004. All of Statistics: A Concise Course in Statistical Inference, New York: Springer- Verlag.
There are 23 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Mehmet Ali Cengiz This is me

Erol Terzi This is me

Talat Şenel This is me

Naci Murat This is me

Publication Date April 1, 2012
Submission Date August 8, 2015
Published in Issue Year 2012 Volume: 12 Issue: 1

Cite

APA Cengiz, M. A., Terzi, E., Şenel, T., Murat, N. (2012). Lojistik Regresyonda Parametre Tahmininde Bayesci Bir Yaklaşım. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 12(1), 15-22.
AMA Cengiz MA, Terzi E, Şenel T, Murat N. Lojistik Regresyonda Parametre Tahmininde Bayesci Bir Yaklaşım. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. April 2012;12(1):15-22.
Chicago Cengiz, Mehmet Ali, Erol Terzi, Talat Şenel, and Naci Murat. “Lojistik Regresyonda Parametre Tahmininde Bayesci Bir Yaklaşım”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 12, no. 1 (April 2012): 15-22.
EndNote Cengiz MA, Terzi E, Şenel T, Murat N (April 1, 2012) Lojistik Regresyonda Parametre Tahmininde Bayesci Bir Yaklaşım. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 12 1 15–22.
IEEE M. A. Cengiz, E. Terzi, T. Şenel, and N. Murat, “Lojistik Regresyonda Parametre Tahmininde Bayesci Bir Yaklaşım”, Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 12, no. 1, pp. 15–22, 2012.
ISNAD Cengiz, Mehmet Ali et al. “Lojistik Regresyonda Parametre Tahmininde Bayesci Bir Yaklaşım”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 12/1 (April 2012), 15-22.
JAMA Cengiz MA, Terzi E, Şenel T, Murat N. Lojistik Regresyonda Parametre Tahmininde Bayesci Bir Yaklaşım. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2012;12:15–22.
MLA Cengiz, Mehmet Ali et al. “Lojistik Regresyonda Parametre Tahmininde Bayesci Bir Yaklaşım”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 12, no. 1, 2012, pp. 15-22.
Vancouver Cengiz MA, Terzi E, Şenel T, Murat N. Lojistik Regresyonda Parametre Tahmininde Bayesci Bir Yaklaşım. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2012;12(1):15-22.