This work proposes a new statistical distribution that can fit real-world data more accurately than many other existing models. The new distribution, which has one scale and one shape parameter, is called an inverted Pham distribution. It can model data with upside-down bathtub or decreasing hazard rate shapes. Order statistics and moments are two of the primary characteristics of the inverted Pham distributions that are examined. Eight classical estimation methods are considered to estimate the model parameters. To investigate the accuracy of the various estimation methodologies, a simulation study is conducted. Based on the criteria of mean square error, mean absolute bias, and mean relative error, the numerical results demonstrated that the maximum likelihood estimates, followed by the maximum product of spacing estimates, outperformed other classical estimation methods. By looking at two actual data sets, one based on failure times of mechanical components and the other consisting of diamond size distribution in South-West Africa, the appropriateness of the inverted Pham model and how it compares with some competitive models are demonstrated. The results of two applications based on some goodness of fit criteria, including Akaike information, Anderson-Darling, Cramér-von Mises, and Kolmogorov–Smirnov statistics, showed that the inverted Pham distribution outperformed commonly used distributions like inverted Lomax, inverted Chen, inverted Weibull, inverted gamma, inverted Nadarajah-Haghighi, inverted exponentiated Pareto, generalized inverted exponential, exponentiated inverted exponential, and generalized inverted half-logistic when analyzing real data sets.

This study enables the simultaneous estimations of the unknown parameters of two inverted Nadarajah–Haghighi populations. The estimations are conducted using data collected from joint progressively type-II censored samples from the populations of interest. The estimations are based on the assumption that the parameters of the two populations are unknown and unequal. To achieve this, we use classical and Bayesian estimation methods to obtain point and interval estimates for the various unknown parameters. The maximum likelihood and approximate interval estimates are derived from the classical perspective. From the Bayesian perspective, both Bayes estimates and credible intervals are acquired using gamma prior distributions. The Bayes estimates are calculated using squared error loss and by sampling from the posterior distribution through the Markov chain Monte Carlo procedure. All point and interval estimates are validated through a simulation study. In addition, two physical applications are provided to demonstrate the practical significance of the methodologies employed.

Nowadays, the increasing demands of smart cities require innovative technologies to optimize resource utilization and minimize costs. This research focuses on the construction sector, specifically the production of advanced high-strength steel. We address practical application problems from the iron and steel factory in Annaba, Algeria, known as fuzzy multi-objective linear programming problems. Two methods are applied: a mathematical model and Artificial Neural Networks (ANNs). Results indicate that the ANN approach offers significant time and effort savings.

In the case of trustworthy products, accelerated life tests are crucial techniques used to gather information regarding the lifetime of the target population with a shorter time frame compared to routine investigations. The received data in higher stress levels than normal are then used to predict the reliability of the product under consideration in regular working circumstances. In this paper, the constant-stress accelerated life tests are employed when the data are obtained through a progressive Type-II censoring strategy from Nakagami populations. Besides estimating the model parameters, the reliability function under the usual use conditions is predicted using four estimation procedures. The maximum likelihood, least squares, weighted least squares and maximum product of spacing estimation approaches are utilized for estimation purposes. In addition, two interval estimation methods are considered utilizing the asymptotic features of the maximum likelihood and maximum product of spacing estimates. A simulation investigation is conducted to evaluate the efficiency of diverse point and interval estimations beneath various strategies involving sample sizes, effective number of failures, and removal designs. From a practical perspective, a pair of datasets are explored to confirm the effectiveness of the theoretical findings.

Background/Objectives: Obesity is currently considered a public health problem in both developed and developing countries. Gender- and age-specific body mass index (BMI) growth standards or references are particularly effective in monitoring the global obesity pandemic. This study aimed to report disparities in age-, gender- and ethnic-specific statistical estimates of overweight and obesity for 2–18 years aged Pakistani children and adolescents using the World Health Organization (WHO), the Center for Disease Control (CDC) 2000 references, the International Obesity Task Force (IOTF) and Pakistani references for BMI. Methods: The study used secondary data of 10,668 pediatric population, aged 2–18 years. Demographic information like age (years), gender, city and anthropometric examinations, i.e., height (cm) and weight (kg) were used in this study. The recommended age- and gender-specific BMI cut-offs of the WHO, CDC 2000 and the IOTF references were used to classify the children sampled as overweight and obese. For the Pakistani reference, overweight and obesity were defined as BMI-for-age ≥ 85th percentile and BMI-for-age ≥ 95th percentile, respectively. Cohen’s κ statistic was used to assess the agreement between the international references and local study population references in the classification of overweight/obesity. Results: The statistical estimates (%) of the participants for overweight and obesity varied according to the reference used: WHO (7.4% and 2.2%), CDC (4.9% and 2.1%), IOTF (5.2% and 2.0%) and Pakistan (8.8% and 6.0%), respectively; suggesting higher levels of overweight and obesity prevalence when local study references are used. The Kappa statistic shows a moderate to excellent agreement (κ ≥ 0.6) among three international references when classifying child overweight and obesity and poor agreement between local references and the WHO (0.45, 0.52), CDC (0.25, 0.50) and IOTF references (0.16, 0.31), for overweight and obesity, respectively. Conclusions: The results of the study showed a visible difference in the estimates of excess body weight after applying the WHO, CDC, IOTF and local BMI references to the study population. Based on the disparity results and poor agreement between international references and the local study reference, this study recommends using local BMI references in identifying children with overweight and obesity.