Statistical modeling study demonstrates improved flexibility for skewed and heavy-tailed data using a trigonometric transmuted half-logistic model, indicating enhanced fit in empirical applications.
This article introduces a novel transmuted half-logistic distribution by incorporating trigonometric functions into its transformation structure. The proposed distribution extends the classical half-logistic model, enhancing its effectiveness in capturing skewness and heavy-tail behaviour in data. We derive its statistical properties, moments, reliability measures and so on. Various parametric estimation procedures are employed to estimate the parameters, and simulation experiments are conducted to study their efficiency. In addition, the applicability of this model is demonstrated through real-world data analysis, showcasing its superiority over several established distributions based on various measures of criterion. AMS Subject Classification: 62E15, 62E10, 60E05
No takes yet. Share an insight, caveat, or question.
Purnima et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: