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This work addresses the weighted inverse Ailamujia distribution, which is an expansion of the inverse Ailamujia distribution (WIAD). Several distributional characteristics of the established distribution have been deduced and evaluated. The explicit expression of distinct ageing factors has been achieved. Several plots depict the behaviour of the probability density function (PDF) and the cumulative distribution function (CDF). The parameters of the formed distribution were estimated using the wellknown maximum likelihood methodology. Furthermore, the likelihood ratio test of the stated distribution was achieved. Eventually, two examples are offered to exemplify the effectiveness of the formulated model. [ABSTRACT FROM AUTHOR]

The family of distributions which are derived from baseline distributions drag the attention of researchers from past recent years. In this paper we introduce an extension of Gumbel type-II by using transmutation approach suggested by Shaw and Buckley (2007). Several distributional properties of the explored distribution, such as moments, moment generating function, quantile function, reliability analysis, order statistics, and Renyi entropy, have been discussed. Various diagrams depict the behaviour of the probability density function (pdf) and cumulative distribution function (cdf). The parameters of the explored distribution are calculated by using the well-known maximum likelihood procedure. Eventually, the utility of the explored distribution is shown using real life data sets. [ABSTRACT FROM AUTHOR]

In this work an extension of Erlang distribution have been introduced, which is in fact a generalization of Erlang distribution. This generalization is derived by applying power transformation technique and it gives more flexibility to investigate complex real life data. The distinct structural properties of the formulated distribution including moments, moment generating function, skewness, kurtosis, incomplete moments, mode, median, order statistics, different measure of entropies, mean deviations, Bonferroni and Lorenz curves have been discussed. In addition expressions for survival function, hazard rate function, reverse hazard rate function and mean residual function are obtained explicitly. The behaviour of probability density function (p.d.f) and cumulative distribution function (c.d.f) are illustrated through different graphs. The estimation of the established distribution parameters are performed by maximum likelihood estimation method. Eventually the versatility of the established distribution is examined through two real life data sets related to engineering and medical science. [ABSTRACT FROM AUTHOR]