The Inverse-Power Burr–Hatke-G Family: Properties and Inference with Real-Life Applications

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Abstract This paper introduces a new generator called the inverse-power Burr–Hatke-G (IPBH-G) family. The special models of the IPBH-G family accommodate different monotone and nonmonotone failure rates, so it turns out to be quite flexible family for analyzing non-negative real-life data. We provide three special sub-models of the family, and derive its key mathematical properties. The parameters of the special IPBH-exponential model are explored using some frequentist approaches of estimation. Numerical simulations are performed to compare and rank the proposed methods based on partial and overall ranks. The superiority of the IPBH-exponential model over other distributions is illustrated empirically by means of three real-life data sets from applied sciences including industry, medicine, and agriculture.

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