• JAVED ALI KHAN
• Dr.Najmus Sehar
• Dr.Arifa Khaoon

This paper introduces the Hierarchical Adaptive Regression Model (HARM), a novel and robust statistical framework designed to enhance both predictive accuracy and interpretability in datasets characterized by complex, multi-level hierarchical structures. HARM innovatively integrates hierarchical linear modeling techniques with adaptive regularization methods, enabling dynamic and level-specific feature selection that respects the nested nature of the data. This dual approach effectively addresses common challenges such as correlated errors within clusters, heterogeneity across groups, and the presence of irrelevant or redundant predictors at different hierarchy levels. The model is particularly well-suited for applications in diverse fields including social sciences, healthcare, marketing analytics, and education research, where data are naturally organized in nested formats. Extensive simulation studies and real-world case analyses demonstrate that HARM consistently outperforms traditional hierarchical regression and standard regularization approaches, achieving superior prediction accuracy, enhanced model sparsity, and improved interpretability without sacrificing computational efficiency.

Adaptive Regression

"Introduction to Adaptive Regression Model (HARM),", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 3, page no.d387-d390, March-2026, Available :http://www.jetir.org/papers/JETIR2603345.pdf

"Introduction to Adaptive Regression Model (HARM),", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 3, page no. ppd387-d390, March-2026, Available at : http://www.jetir.org/papers/JETIR2603345.pdf