Enhanced Poverty Assessment through Advanced Analytical Models within the Malaysia MADANI Framework

This paper presents a research framework for an innovative poverty assessment methodology aligned with the Malaysia MADANI Framework's objectives of eradicating poverty and promoting inclusive economic growth.  Traditional approaches to household categorization often neglect critical demographic variables and the unequal distribution of income, leading to an incomplete understanding of poverty.  To address these limitations, the framework integrates mixture ordinal regression models with machine learning algorithms, leveraging the strengths of statistical modeling and advanced predictive analytics.  By conceptualizing a multi-layered analytical model, the proposed approach provides a more comprehensive and nuanced understanding of poverty dynamics within Malaysia's diverse socio-economic landscape.  The model aims to deliver detailed insights essential for designing effective and targeted policy interventions.  This framework is expected to overcome shortcomings of conventional methods, offering policymakers a robust and adaptable tool for poverty alleviation.  Ultimately, the research seeks to advance the Malaysia MADANI Framework's vision of inclusive growth and development.