Harris Extended Power Lomax Distribution: Properties, Inference and Applications

Abstract

In this work, we present a five-parameter life time distribution called Harris power Lomax (HPL)  distribution which is obtained by convoluting the Harris-G distribution and the Power Lomax distribution. When compared to the existing distributions, the new distribution exhibits a very flexible probability functions; which may be increasing, decreasing, J, and reversed J shapes been observed for the probability density and hazard rate functions. The structural properties of the new distribution are studied in detail which includes: moments, incomplete moment, Renyl entropy, order statistics, Bonferroni curve, and Lorenz curve etc. The HPL  distribution parameters are estimated by using the method of maximum likelihood. Monte Carlo simulation was carried out to investigate the performance of MLEs. Aircraft wind shield data and Glass fibre data applications demonstrate the applicability of the proposed model.

Analyzing Non-Monotonic Hazard Rate Data Using the Harris Extended Inverted Lindley Distribution

“…respectively, where the median is represented by ∼ µ = Median(X) and can be obtained via (17), while µ ′ 1 = Mean(X) can be obtained via (23). The quantity MD 1 (X) and MD 2 (x) can be calculated by the following relationship.…”

“…where G(t) is cdf of HEIL distribution can be obtained by the relation G(t) = 1 − Ḡ(t). It should be noted that µ = µ ′ 1 equal the first about the origin is obtained in by taking r = 1 in (23). Similarly, the Lorenz Curve is defined by…”

“…Jose et al [22] developed reliability test plans for Harris extended Weibull distribution. Harris power Lomax distribution is proposed by Ogunde et al [23] which is obtained by convoluting the Harris-G distribution and the power Lomax distribution. Sophia et al [24] proposed the Harris extended two-parameter Lindley Distribution, and Lishamol et al [30] presented the Harris extended modified Lindley Distribution for environmental data.…”

A Note on Harris Extended Generalized Exponential Distribution

“…This method of generalization of the baseline distribution has been explored by many authors which includes Chahkandi and Ganjali (2009) and Morais andBakouch (2011), Lu andShi (2012), Ristic andNadarajah (2011), Pinho et al (2012), Pinho et al (2015), Jose and Paul (2018), Jose et al(2018). More recently, Sophia et al(2021), andOgunde et al (2021), among many others.…”