In this article, a new two-parameter discrete power-Ailamujia (DsPA) distribution is derived using the survival discretization technique. Some key distributional properties and reliability measures are explored in closed forms, such as probability generating function, first four moments and mean residual life. The DsPA parameters are estimated using the maximum likelihood approach. The performance of this estimation method is assessed via a simulation study. The flexibility of the DsPA distribution is shown using three count datasets. The DsPA distribution provides a better fit than some recent discrete models such as the discrete Burr-Ⅻ, uniform Poisson–Ailamujia, Poisson, discrete-Pareto, discrete-Rayleigh, discrete inverse-Rayleigh, and discrete Burr–Hutke distributions.

In this paper, we proposed a novel and flexible lifetime model, the generalized Kavya–Manoharan Weibull distribution, which can be interpreted as a proportional reversed hazard model. The most remarkable feature of the proposed model is its ability to effectively capture a wide range of hazard rate patterns using only three parameters. These include decreasing, J-shaped, reverse J-shaped, and increasing patterns, as well as key nonmonotonic shapes such as the bathtub, modified bathtub, and upside-down bathtub shapes. Additionally, its density can exhibit right-skewness, left-skewness, symmetry, and reversed-J shapes. We explored several distributional properties of the proposed model and estimated its parameters using eight methods. The effectiveness of these estimators was validated through extensive simulation studies. Furthermore, we assessed the versatility of the proposed distribution using three real-world datasets, demonstrating its exceptional capacity to fit the data accurately. Our results indicated that the proposed distribution outperforms several existing generalizations of the Weibull distribution in terms of fit quality.