Testing for A Change Point by Model Selection Tools in Linear Regression Models

  • Yoon, Yong-Hwa (Department of Statistics, Taegu University) ;
  • Kim, Jong-Tae (Department of Statistics, Taegu University) ;
  • Cho, Kil-Ho (Department of Statistics, Kyungpook National University) ;
  • Shin, Kyung-A (Department of Statistics, Kyungpook National University)
  • Published : 2000.12.01

Abstract

Several information criterions, Schwarz information criterion (SIC), Akaike information criterion (AIC), and the modified Akaike information criterion ($AIC_c$), are proposed to locate a change point in the multiple linear regression model. These methods are applied to a stock Exchange data set and compared to the results.

Keywords

References

  1. Second International Symposium in Information Theory Information theory and an extension of the maximum likelihood principle Akaike, H.
  2. IEEE Trans. Automat. Control v.19 A new look at the statistical model identificaion Akaike, H.
  3. Journal of the Royal Statistical Society B Techniques for testing the constancy of regression relationships over time(with discussion) Brown, R.L.;Durbin, J.;Evans, J.M.
  4. Communications in statistics - Theory and Method v.28 Generalizing the derivation of the Schwarz information criterion Cavanaugh, J.E.;Neath, A.A.
  5. Communications in statistics - Theory and Method v.27 Testing for a change point in linear regression models Chen, J.
  6. Operators and Stochastic Equations v.3 Likelihood procedure for testing change points hypothesis for multivariate Gaussian model Chen, J.;Gupta, A.K.
  7. Journal of American Statistical Association v.92 Testing and locating variance changepoints with application to stock price Chen, J.;Gupta, A.K.
  8. Technometrics v.22 Some Baysian inference for a changing linear model Choy, J.H.;Broemeling, L.D.
  9. Journal of American Statistical Association v.70 Baysian analysis of a switching regression model : Known number of regimes Ferreira, P.E.
  10. Biometrika v.84 Modified AIC and Cp in multivariate linear regression Fujikoshi, Y.;Satoh, K.
  11. Computational Statistics v.11 Detecting changes of mean in multidensional normal sequences with applications to literature and geology Gupta, A.K.;Chen, J.
  12. Communication in Statistics v.18 A U-I approach to retrospective testing for shifting parameters ina linear model Hawkins, D.L.
  13. Journal of Economics v.19 A Baysian analysis of a switching linear model Holbert, D.
  14. Communications in statistics - Theory and Method v.28 An iterative Approach to variable selection based on the Kullback-Leibler information Hughes, A.W.;King, M.L.
  15. Biometrika v.76 Regression and time series model selection in small samples Hurvich, C.M.;Tsai, C.L.
  16. Journal of American Statistical Association v.90 Bayes factor Kass, R.E.;Rafferty, A.E.
  17. Journal of American Statistical Association v.90 A reference Bayesian test for nested hypotheses and its relationship to the Schwarz criterion Kass, R.E.;Wasserman, L.
  18. Journal of American Statistical Association v.53 The estimation of the parameters of a linear regression system obeys two separate regimes Quandt, R.E.
  19. Journal of American Statistical Association v.55 Tests of the hypothesis that a linear regression system obeys two separate regimes Quandt, R.E.
  20. Annals of Statistics v.6 Estimating the dimension of a model Schwarz, G.
  21. The Annals of Statistics v.3 On test for detecting change in mean Sen, A.K.;Srivastava, M.S.
  22. Journal of American Statistical Association v.81 Likelihood ratio tests for a change in the multivariate nomal mean Srivastava, M.S.;Worsley, K.J.
  23. Communications in statistics - Theory and Method v.A7 Further analysis of the data by Akaike's information criterion and the finite corrections Sugiura, N.
  24. Journal of American Statistical Association v.74 On the likelihood ratio test for a shift in location of normal populations Vorsley, K.J.