Advancing Hierarchical Model: Evaluating Performance, Interpretability and Implications

Seth Opoku Larbi; Apaka Rangita; Joyce Otieno

doi:10.36948/ijfmr.2024.v06i06.30796

Advancing Hierarchical Model: Evaluating Performance, Interpretability and Implications

Author(s)	Seth Opoku Larbi, Apaka Rangita, Joyce Otieno
Country	Kenya
Abstract	Hierarchical data structures emerge when observations are nested within higher-level units or clusters. Existing research often ignores the hierarchical structure of data leading to biased estimates, suboptimal model selection, and challenges in identifying important predictors and dependencies. This study aims to contribute to hierarchical frameworks by improving challenges with the interpretability of the random effect, scalability, and computation feasibility in the traditional hierarchical Bayesian model. The authors’ model is an advancement of the Standard hierarchical Bayesian model which introduced a unique variable to the model and parameters to the random effects. The advancements in hierarchical modelling by the authors have significantly improved the accuracy, reliability, and interpretability of the model analysis. Hierarchical Bayesian Information Criteria (HBIC) is the method of selecting variables in the models. The findings of the research indicated that the introduction of Phi (ϕ_j) and Psi (ψ_j) as shrinkage parameters are instrumental in the regulation of parameter estimates towards a common value, resulting in more accurate estimation and less overfitting in the Hierarchical Bayesian Model as well as accounting for the presence of heterogeneity. The introduction of the unique variable (z) allows the model to capture cluster-specific effects associated with the (z). Lastly, the study found that the authors' innovative model outperformed the standard model by improving the accuracy, interpretability, scalability, and regularisation through shrinkage parameters and innovative (z) as the contextual variable.
Keywords	Shrinkage, Hierarchical, Parameters, Prior, Posterior, Uncertainty, Lasso, Unique Variable
Field	Mathematics > Statistics
Published In	Volume 6, Issue 6, November-December 2024
Published On	2024-11-25
Cite This	Advancing Hierarchical Model: Evaluating Performance, Interpretability and Implications - Seth Opoku Larbi, Apaka Rangita, Joyce Otieno - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.30796
DOI	https://doi.org/10.36948/ijfmr.2024.v06i06.30796
Short DOI	https://doi.org/g8r8kv

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Advancing Hierarchical Model: Evaluating Performance, Interpretability and Implications

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