Half Logistic Inverted Nadarajah–Haghighi Distribution under Ranked Set Sampling with Applications

In this paper, we present the half logistic inverted Nadarajah–Haghigh (HL-INH) distribution, a novel extension of (INH) distribution. The probability density function (PDF) for HL-INH distribution might have unimodal, right skewness, or heavy-tailed shape numerous parameter values; however, forms hazard rate (HRF) may be decreasing. Four specific entropy measurements were investigated. Some useful expansions Several statistical and computational features calculated. Using simple (SRS) ranked set sampling (RSS), parameters estimated using maximum likelihood (ML) technique. A simulation analysis was executed in order to determine model SRS RSS methods, shown more efficient than SRS. We demonstrate that is adaptable INH other distributions when utilizing three real-world datasets.