Autoregressive models: statistical inference and applications

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2002
Türker, Özlem
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The autoregressive model Yt - + Ym = ji + 8 (5Q - * X,,,) + e» (l<t<n) has many applications in agricultural, biological and biomedical sciences besides business and economics. Since the model is non-linear due to the parameter 5(|), it has not been easy to estimate the unknown parameters \x, 5, (j) and a, a2 being the variance of the iid innovations st. Most of the literature on this topic has hinged on the assumption of iiinormality. However, there is now a realization that non-normal distributions occur so frequently in practice. By the use of the modified likelihood methodology, the model has been opened up to non-normal innovations. The aim of this study is to extend the methodology under non-normality to various independent sources of information and to develop robust and efficient statistics for testing whether the parameter vector remains the same from one source to another.
Autoregression (Statistics), Regression analysis, Robust control, Parameter estimation, Maximum Likelihood Estimators (MLE), Least Squares Estimators (LSE), Modified Likelihood Estimators (MMLE), Autoregression, Robustness, Non-normality
https://hdl.handle.net/11511/12869
Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats
Ö. Türker, “Autoregressive models: statistical inference and applications,” Ph.D. - Doctoral Program, Middle East Technical University, 2002.
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