Enhanced parameter estimation for Lomax distribution using a contemporary triangular fuzzy ranking method.

This study presents a novel approach to parameter estimation for the Lomax distribution using a contemporary triangular fuzzy ranking method. The Lomax distribution is critical in reliability analysis and lifetime data modeling but often faces challenges when handling uncertain or imprecise data. Our proposed ranking function for Triangular Fuzzy Numbers (TFNs) enhances the estimation process in fuzzy environments, enabling better interpretation of incomplete datasets.

Statistical fractal analysis in testing the Hypotheses with imprecise data.

A new statistical hypothesis testing procedure using fractal analysis, incorporating the concept of lacunarity for interval population parameters when the sample data are real intervals, is introduced. The decision rules for accepting or rejecting the null and alternative hypotheses are provided, along with testing procedures and numerical examples. Additionally, the proposed tests are extended to statistical hypotheses involving fuzzy data samples with lacunarity.