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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
The methodology is illustrated using a real data set.
📌 3 Key Takeaways
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Objective & Hypothesis:The traditional theory of analysis of covariance (ANCOVA) is based on normality assumption, while in many real world applications the data violate normality and this theory is not adequate.
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Key Findings & Data:A simulation study is performed to assess the performance of the proposed model.
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Conclusion & Impact:The methodology is illustrated using a real data set.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Analysis of covariance · skew normal · maximum likelihood
Abstract
The traditional theory of analysis of covariance (ANCOVA) is based on normality assumption, while in many real world applications the data violate normality and this theory is not adequate. In this paper, we expand a model for analysis of covariance with a skew normal response variable. The maximum likelihood estimates of the model parameters are provided via an EM algorithm. We also developed asymptotic confidence intervals for parameters. A simulation study is performed to assess the performance of the proposed model. The methodology is illustrated using a real data set.
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Cite This Article
FALLAH, A., GOODARZİ, Z. (2022). Analysis of covariance by assuming a skew normal distribution for response variable. Hacettepe Journal of Mathematics and Statistics, Cilt: 45 - Sayı: 6, 1805–1818. https://ajindex.com/articles/530044