İstatistik Bilim Dalı Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/4382

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  • Article
    Citation - Scopus: 1
    Linear Contrasts in One-Way Classification Ar(1) Model With Gamma Innovations
    (Hacettepe Univ, Fac Sci, 2016) Senoglu, Birdal; Bayrak, Ozlem Turker
    In this study, the explicit estimators of the model parameters in oneway classification AR(1) model with gamma innovations are derived by using modified maximum likelihood (MML) methodology. We also propose a new test statistic for testing linear contrasts. Monte Carlo simulation results show that the MML estimators have higher efficiencies than the traditional least squares (LS) estimators and the proposed test has much better power and robustness properties than the normal theory test.
  • Article
    Citation - Scopus: 1
    Inference of Autoregressive Model With Stochastic Exogenous Variable Under Short-Tailed Symmetric Distributions
    (Springer international Publishing Ag, 2018) Bayrak, Ozlem Tuker; Akkaya, Aysen Dener
    In classical autoregressive models, it is assumed that the disturbances are normally distributed and the exogenous variable is non-stochastic. However, in practice, short-tailed symmetric disturbances occur frequently and exogenous variable is actually stochastic. In this paper, estimation of the parameters in autoregressive models with stochastic exogenous variable and non-normal disturbances both having short-tailed symmetric distribution is considered. This is the first study in this area as known to the authors. In this situation, maximum likelihood estimation technique is problematic and requires numerical solution which may have convergence problems and can cause bias. Besides, statistical properties of the estimators can not be obtained due to non-explicit functions. It is also known that least squares estimation technique yields neither efficient nor robust estimators. Therefore, modified maximum likelihood estimation technique is utilized in this study. It is shown that the estimators are highly efficient, robust to plausible alternatives having different forms of symmetric short-tailedness in the sample and explicit functions of data overcoming the necessity of numerical solution. A real life application is also given.