Hypothesis testing in one-way classification AR(1) model with Student’s t innovations: An application to a real life data

2017-05-26
Yıldırım, Özgecan
Yozgatlıgil, Ceylan
Şenoğlu, Birdal
In this study, we estimate the model parameters in one-way classification AR (1) model when the distribution of the error terms is independently and identically distributed (iid) Student’s t. Maximum likelihood (ML) methodology is used in the estimation procedure. We also introduce the F statistic based on the ML estimators of the parameters for testing the equality of the treatment means. See also Yıldırım (2017) (M.S. Thesis, METU, Ankara, Continue) and Şenoğlu and Bayrak (2016) (Linear Contrasts in one-way classification AR (1) model with gamma innovations, Hacettepe Journal of Mathematics and Statistics 45(6), pp. 1743-1754). Then we compare the efficiencies of the ML estimators of the unknown parameters with the corresponding LS estimators via an extensive Monte Carlo simulation study. Simulation results showed that the ML estimators in all simulation scenarios are more efficient than the corresponding LS estimators as expected. Powers of the test statistic based on the ML estimators are also compared with the corresponding test statistic based on LS estimators. At the end of the study, we analyse a real life data set in order to present the implementation of the methodology. For this purpose, we compare our results with the corresponding normal theory results based on this data.
3rd International Researchers, Statisticians And Young Statisticians Congress, 24-26 May 2017

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Citation Formats
Ö. Yıldırım, C. Yozgatlıgil, and B. Şenoğlu, “Hypothesis testing in one-way classification AR(1) model with Student’s t innovations: An application to a real life data,” presented at the 3rd International Researchers, Statisticians And Young Statisticians Congress, 24-26 May 2017, Konya, Turkey, 2017, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/86800.