Statistical mirroring: A robust method for statistical dispersion estimation.

This study introduces statistical mirroring as an innovative approach to statistical dispersion estimation, drawing inspiration from the Kabirian-based isomorphic optinalysis model, aimed at enhancing robustness and mitigating biases in estimation methods. Beyond scale-invariant characteristics, the proposed estimators emphasize scaloc-invariant robustness, thereby addressing a critical gap in dispersion estimation. By highlighting statistical meanic mirroring, alongside other forms of proposed statistical mirroring, the study underscores the adaptability and customization potential.

Kabirian-based optinalysis: A conceptually grounded framework for symmetry/asymmetry, similarity/dissimilarity and identity/unidentity estimations in mathematical structures and biological sequences.

This paper introduces "Kabirian-based optinalysis (KBO)," a pioneering framework that addresses the longstanding challenges in estimating symmetry/asymmetry, similarity/dissimilarity, and identity/unidentity within mathematical structures and biological sequences. The existing methods often lack a strong theoretical foundation, leading to inconsistencies and limitations. Kabirian-based optinalysis draws inspiration from isomorphism and automorphism, providing a theoretically grounded framework that unifies estimation methodologies.