The inverse power novel two-parameter quadratic exponential distribution: Case studies in the finance, radiation, and environmental sciences

In this paper, we introduce a new extension model with three parameters as a modification of the novel two-parameter quadratic exponential distribution (NTPQE). Applying the inverse power transformation procedure on the NTPQE distribution, the produced distribution is termed the inverse power novel two-parameter quadratic exponential (IPNTPQE) distribution. Its efficacy in generating both symmetric and asymmetric probability density functions renders it an ideal option for modeling lifetime phenomena. The relevant hazard rate function is suitable for many real data forms, exhibiting one of the following shapes: growing, decreasing, reverse J-shape, or inverted shape. The paper examines several significant properties, including moments, inverse moments, incomplete moments, Lorenz and Bonferroni curves, mean, variance, coefficients of skewness and kurtosis, order statistics, and quantile function. Seven efficient estimation strategies are employed to ascertain the parameters of the IPNTPQE distribution. A Monte Carlo simulation is employed to assess the efficacy of the derived estimates. The numerical findings indicate that the maximum product of spacings strategy consistently outperforms other methods in accuracy when predicting the pertinent parameters. Four actual datasets pertaining to the radiation, financial and environmental sectors are included in the practical application to illustrate the adaptability of the new model relative to existing alternatives. The IPNTPQE distribution offers significant advantages over traditional distributions, making it a crucial tool for professionals dealing with extreme values and infrequent occurrences. Its computational performance further differentiates it, enabling more thorough and accelerated analysis of extensive datasets. This empirical study reinforces the effectiveness and versatility of the IPNTPQE model in analyzing the complexities of various datasets, hence providing valuable insights for professionals across multiple fields.