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Probabilities of Correctly Classifying Belong to Logistic Regression Model and Discriminant Analysis with four Variables Compares with Simulation Technique in Condition that Different Mean Vector and Different Covariance Matrix

Year 2002, Volume: 1 Issue: 3, 13 - 24, 16.12.2002

Abstract

In this study, to show that the effect of various combination mean vectors, variance and covariance structure of continuous variables on the true classification proportions each population was made a simulation study. Simulation results show that both linear discriminant analysis and logistic regression analysis same.

References

  • AGRESTI, A. (1990), Categorical data analysis, John Wiley & Sons, 558 P., New York-USA.
  • ALHO, J. M. (1990), “Logistic regression in capture-recapture models”, Biometrics, 46, 623-635.
  • ANDERSON, J. A. (1972), “Separate sample logistic discrimination”, Biometrica, 59(1), 19-35.
  • ÇAMDEVİREN, H. (2000), Logistik Regresyon ve Diskriminant Analizi, A.Ü. Fen Bilm. Enst. Doktora Tezi (Yayınlanmamış), 185s.
  • EVERITT, B.S. (1992), The analysis of contingency tables, Chapman&Hall, Second Edition, 164p., London-UK.
  • FEARS, T.R. and BROWN, C.C. (1986), “Logistic regression methods for redrospective case-control studies using complex sampling procedures”, Biometrics, 42, 955-960.
  • HOSMER, T., HOSMER, D., and FISHER, L. (1983), “A Comparison of the maximum likelihood and discriminant function estimators of the coefficients of the logistic regression model for mixed continuous and discrede variables”, Commun. Statist.- Simula. Computa., 12(1), 23-43.
  • HOSMER, D.W., JAVANOVIC, B. and LEMESHOW, S. (1989), “Best subsets logistic regression”, Biometrics, 45, 1265-1270.
  • JOHNSON, R.A. and WICHERN, D.W. (1982), Applied multivariate statistical analysis, Prentice-Hall, INC., Englewood Cliffs, 594 P., New Jersey-USA.
  • KNOKE, J.D. (1982), “Discriminant analysis with discrete and continuous variables”, Biometrics, 38, 191-200.
  • QIN, J. and ZHANG, B. (1997), “A goodness-of-fit test for logistic regression models based on case-control data”, Biometrika, 84(3), 609-618.
  • SCHMITZ, P.I.M., HABBEMA, J.D.F., HERMANS, J. and RAATGEVER, J.W. (1983) “Comparative performance of four discriminant analysis methods for mixtures of continuous and discrede variables”, Commun. Statist.-Simula. Computa., 12(6), 727-751.
  • STEVENS, J. (1986), Applied multivariate statistics for the social sciences, Hillsdale, 509 P., New Jersey-USA.

Farklı Ortalama Vektörü ve Farklı Kovaryans Matrisi Koşullarında Dört Değişkenli Lojistik Regresyon Modeli ve Diskriminant Analizine Ait Doğru Sınıflandırma Olasılıklarının Simülasyon Tekniği Yardımıyla Karşılaştırılması

Year 2002, Volume: 1 Issue: 3, 13 - 24, 16.12.2002

Abstract

Bu çalışmada, farklı değişken yapısına sahip iki veri setinde, sürekli değişkenlere ait ortalama, varyans ve kovaryanslardaki değişmenin her bir populasyona doğru sınıflandırma olasılıkları üzerine etkisini araştırmak amacıyla bir simülasyon çalışması yapılmıştır. Bu çalışma sonucunda, her bir populasyona doğru sınıflandırma olasılıkları bakımından doğrusal diskriminant analiz ve lojistik regresyon analizinin benzer sonuçlar verdiği görülmüştür.

References

  • AGRESTI, A. (1990), Categorical data analysis, John Wiley & Sons, 558 P., New York-USA.
  • ALHO, J. M. (1990), “Logistic regression in capture-recapture models”, Biometrics, 46, 623-635.
  • ANDERSON, J. A. (1972), “Separate sample logistic discrimination”, Biometrica, 59(1), 19-35.
  • ÇAMDEVİREN, H. (2000), Logistik Regresyon ve Diskriminant Analizi, A.Ü. Fen Bilm. Enst. Doktora Tezi (Yayınlanmamış), 185s.
  • EVERITT, B.S. (1992), The analysis of contingency tables, Chapman&Hall, Second Edition, 164p., London-UK.
  • FEARS, T.R. and BROWN, C.C. (1986), “Logistic regression methods for redrospective case-control studies using complex sampling procedures”, Biometrics, 42, 955-960.
  • HOSMER, T., HOSMER, D., and FISHER, L. (1983), “A Comparison of the maximum likelihood and discriminant function estimators of the coefficients of the logistic regression model for mixed continuous and discrede variables”, Commun. Statist.- Simula. Computa., 12(1), 23-43.
  • HOSMER, D.W., JAVANOVIC, B. and LEMESHOW, S. (1989), “Best subsets logistic regression”, Biometrics, 45, 1265-1270.
  • JOHNSON, R.A. and WICHERN, D.W. (1982), Applied multivariate statistical analysis, Prentice-Hall, INC., Englewood Cliffs, 594 P., New Jersey-USA.
  • KNOKE, J.D. (1982), “Discriminant analysis with discrete and continuous variables”, Biometrics, 38, 191-200.
  • QIN, J. and ZHANG, B. (1997), “A goodness-of-fit test for logistic regression models based on case-control data”, Biometrika, 84(3), 609-618.
  • SCHMITZ, P.I.M., HABBEMA, J.D.F., HERMANS, J. and RAATGEVER, J.W. (1983) “Comparative performance of four discriminant analysis methods for mixtures of continuous and discrede variables”, Commun. Statist.-Simula. Computa., 12(6), 727-751.
  • STEVENS, J. (1986), Applied multivariate statistics for the social sciences, Hillsdale, 509 P., New Jersey-USA.
There are 13 citations in total.

Details

Primary Language Turkish
Subjects Statistical Theory
Journal Section Research Articles
Authors

Handan Çamdeviren This is me

E. Arzu Kanık This is me

Fikret Gürbüz This is me

Publication Date December 16, 2002
Published in Issue Year 2002 Volume: 1 Issue: 3

Cite

APA Çamdeviren, H., Kanık, E. A., & Gürbüz, F. (2002). Farklı Ortalama Vektörü ve Farklı Kovaryans Matrisi Koşullarında Dört Değişkenli Lojistik Regresyon Modeli ve Diskriminant Analizine Ait Doğru Sınıflandırma Olasılıklarının Simülasyon Tekniği Yardımıyla Karşılaştırılması. İstatistik Araştırma Dergisi, 1(3), 13-24.