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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
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 normaltheory test.
📌 3 Key Takeaways
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Objective & Hypothesis: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.
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Key Findings & Data:We also propose a new test statistic for testing linear contrasts.
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Conclusion & Impact: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 normaltheory test.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Autoregressive model · linear contrasts · nonnormality
Abstract
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 normaltheory test.
Keywords
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Cite This Article
ŞENOĞLU, B., BAYRAK, Ö. T. (2022). Linear contrasts in one-way classication AR(1) model with gamma innovations. Hacettepe Journal of Mathematics and Statistics, Cilt: 45 - Sayı: 6, 1743–1754. https://ajindex.com/articles/530180