This study examines how ESG disclosure (ESGD) influences firm value (FV) in Saudi Arabia. It also analyzes the moderating roles of audit committee (AC) characteristics and sustainability committees (SC) on this relationship. The sample consists of 100 top non-financial firms listed on the Saudi stock exchange (Tadawul) from 2015 to 2022, yielding 800 firm-year observations. Using pooled ordinary least squares (OLS), fixed effects regression, and GMM methods, the findings indicate a significant positive effect of ESGD on FV. Moreover, all AC characteristics and the sustainability committee positively and significantly strengthened this relationship. These findings carry important implications for investors, regulators, and corporate managers by highlighting how governance structures can influence financial performance, especially in emerging markets. This study contributes to the literature by expanding the discussion on the beneficial impact of AC characteristics and SC on FV within developing economies. Unlike earlier research that mainly focused on the direct link between ESGD and FV, this work underscores the role of governance factors in enhancing that relationship.

This article presents a novel unit distribution called the unit inverse exponentiated Lomax distribution. Some of its key characteristics are carefully examined. The distribution parameters are estimated using both traditional and Bayesian approaches, taking into account the progressive Type II censoring schemes. Using the symmetric loss function and the Markov chain Monte Carlo technique, the Bayesian methodology is investigated to determine the point and credible interval estimates of parameters. In order to select the best progressive censoring scheme, a number of optimization criteria are taken into consideration. According to the selected criteria measures, the simulations showed that the Bayesian estimates outperform the maximum likelihood estimates in terms of accuracy, indicating better parameter estimation precision. Additionally, most coverage probabilities were high, at about 95%. The novel unit distribution flexibility is validated using a three-real data set from different domains.

Journal of Applied Mathematics and Computational Mechanics, Prace Naukowe Instytutu Matematyki i Informatyki, Politechnika Częstochowska, Scientific Research of the Institute of Mathematics and Computer Science, Czestochowa University of Technology

This study introduces and rigorously investigates the novel three-parameter Type II Exponentiated Half-Logistic Exponential (TIIEHLEx) distribution, designed to offer enhanced flexibility for modeling positive-valued data in fields such as hydrology and risk analysis. We first establish the model's theoretical foundation, demonstrating that its probability density function (PDF) is versatile and that its hazard rate function can capture non-monotone risk patterns (bathtub, inverted bathtub, IHR, and DHR), positioning it favorably against models with restricted hazard shapes. A comprehensive simulation study evaluating fourteen non-Bayesian estimation methods confirmed that the Minimum Spacing Absolute Distance Estimator (MSADE) consistently provided the most accurate and reliable parameter estimates (lowest Bias, MSE, and MRE). Applying the TIIEHLEx model to two distinct real-world datasets—March Precipitation and Annual Insurance and Financial Services data—we demonstrated its empirical superiority, as evidenced by the highest Kolmogorov-Smirnov p-values and lowest information criteria compared to several competitor models. This work validates the TIIEHLEx distribution as a highly versatile and robust tool for reliable modeling and parameter estimation of complex data structures.

Unit-bounded distributions are often used to mimic values that are strictly defined inside the interval (0, 1). Despite this, these distributions are more uncommon than those with semi-bounded support (0, ∞). However, many real-life circumstances involve observations with a unit-bounded range, such as proportions, percentages, ratios, rates, and fractions. This study introduces the Log-Inverse Gompertz Distribution (LIGD), derived via a negative exponential transformation of the Inverse Gompertz distribution. The LIGD exhibits flexible density shapes (J, reversed-J, and left-skewed unimodal) and a strictly increasing hazard rate. The study examines statistical aspects and reliability measures such as survival, hazard, cumulative hazard, reversal hazard, quantile functions, median, skewness, kurtosis, and order statistics. The parameters of the proposed model were estimated using maximum likelihood estimation and maximum product of spacing, with a Monte Carlo simulation utilized to assess the efficacy of various estimating approaches. Finally, the proposed model's applicability is illustrated using two real-world data sets. A comparative analysis demonstrates that the proposed model outperforms many existing ones.

In practice, systems that are exposed to rigorous operational environments commonly malfunction. Researchers have yet to fully examine the crucial idea that system failure frequently occurs when these strict operating constraints are reached. This study covers this gap in an analysis of the multi-stress-strength model $\Upsilon=P(U<W<V)$, in which stresses ($U$ $\& V$) and strength ($W$) are defined using the exponentiated Weibull distribution. From an advanced censoring method called generalized progressive hybrid censoring, we obtain the point and interval estimators of $\Upsilon$. The maximum likelihood and Bayesian estimators of $\Upsilon$ under both the symmetric and asymmetric loss functions are obtained. We employ Markov chain Monte Carlo techniques due to the complexity of Bayesian estimators. We also provide Bayesian credible intervals, bootstrap-t intervals, and percentile bootstrap intervals. A simulation study is conducted to evaluate the efficacy of the proposed estimates. Numerical results lead us to the conclusion that the Bayesian estimates based on informative priors outperform classical estimates in terms of biases, mean squared error, and coverage probabilities. Real progressively censored engineering data application of real data is presented to demonstrate the efficacy of the proposed estimators.