EGG QUALITY ASSESSMENT: A MODEL COMPARISON APPROACH USING BAYESIAN MIXED LOGIT, MIXED LOGIT, LOGISTIC REGRESSION AND MULTINOMIAL REGRESSION MODELS

Authors

  • Christian Chinenye Amalahu
    University of Agriculture and Environmental Sciences, Umuagwo image/svg+xml
  • Joy Chioma Nwabueze
    Michael Okpara University of Agriculture, Umudike
  • Chibueze Barnabas Ekeadinotu
    University of Agriculture and Environmental Sciences, Umuagwo image/svg+xml

Keywords:

Bayesian mixed logit, Egg quality, Mixed logit Model

Abstract

This study compares the performance of Bayesian mixed logit, mixed logit, logistic regression, and multinomial regression models in analyzing egg quality. The results show that the Bayesian mixed logit model outperforms traditional models, with egg weights, shell thickness, and shape index emerging as significant determinants of egg quality. The Bayesian mixed logit model's superior performance is evident in its lower AIC, DIC, RMSE, and MAE values. These findings have implications for the poultry industry, highlighting the importance of considering complex relationships between egg quality traits.

Dimensions

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Published

31-05-2025

How to Cite

EGG QUALITY ASSESSMENT: A MODEL COMPARISON APPROACH USING BAYESIAN MIXED LOGIT, MIXED LOGIT, LOGISTIC REGRESSION AND MULTINOMIAL REGRESSION MODELS. (2025). FUDMA JOURNAL OF SCIENCES, 9(5), 110-113. https://doi.org/10.33003/fjs-2025-0905-3656

Issue

Vol. 9 No. 5 (2025): FUDMA Journal of Sciences - Vol. 9 No. 5

Section

Research Articles
Copyright & Licensing

FUDMA Journal of Sciences

How to Cite

EGG QUALITY ASSESSMENT: A MODEL COMPARISON APPROACH USING BAYESIAN MIXED LOGIT, MIXED LOGIT, LOGISTIC REGRESSION AND MULTINOMIAL REGRESSION MODELS. (2025). FUDMA JOURNAL OF SCIENCES, 9(5), 110-113. https://doi.org/10.33003/fjs-2025-0905-3656

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