Accurate estimation techniques are crucial in statistical modeling and reliability analysis, which have significant applications across various industries. The three-parameter Weibull distribution is a widely used tool in this context, but traditional estimation methods often struggle with outliers, resulting in unreliable parameter estimates. To address this issue, our study introduces a robust estimation technique for the three-parameter Weibull distribution, leveraging the probability integral transform and specifically employing the Weibull survival function for the transformation, with a focus on complete data. This method is designed to enhance robustness while maintaining computational simplicity, making it easy to implement. Through extensive simulation studies, we demonstrate the effectiveness and resilience of our proposed estimator in the presence of outliers. The findings indicate that this new technique significantly improves the accuracy of Weibull parameter estimates, thereby expanding the toolkit available to researchers and practitioners in reliability data analysis. Furthermore, we apply the proposed method to real-world reliability datasets, confirming its practical utility and effectiveness in overcoming the limitations of existing estimation methodologies in the presence of outliers.

The sustainable development of the energy sector through the implementation of renewable energy (RE) technology has been a primary concern for the government of Malaysia since the 1990s. Developing RE technology in rural areas is also one of the most important energy strategies. One of the most important requirements for the successful adoption of renewable energy technology is the public acceptance of this technology. This article examines the public acceptance of RE (wind and solar) technologies among rural residents in Mersing, Malaysia. This study is conducted in conjunction with the development of the hybrid wind–solar RE generation system for the Marine Ecosystem Research Centre (EKOMAR), which located in Mersing. To investigate the public acceptance of RE technologies, two surveys were conducted, before and after the installation of the hybrid wind–solar RE generation system. The findings revealed that RE innovations, such as wind and solar energy, were well known among 54.7% and 96.2% of the respondents, respectively, before and after the introduction of EKOMAR. The survey results showed that rural residents generally support RE technologies and EKOMAR RE projects. They were also in line that the use of RE should also be extended to other parts of Mersing, including wind and solar. These findings illustrate that EKOMAR has played a crucial role in educating local residents about RE, wind and solar energy in particular. Residents believe the government plays an important role, particularly in rural areas, in encouraging and enhancing RE technologies.

In the modeling and analysis of reliability data via the Lindley distribution, the maximum likelihood estimator is the most commonly used for parameter estimation. However, the maximum likelihood estimator is highly sensitive to the presence of outliers. In this paper, based on the probability integral transform statistic, a robust and efficient estimator of the parameter of the Lindley distribution is proposed. We investigate the relative efficiency of the new estimator compared to that of the maximum likelihood estimator, as well as its robustness based on the breakdown point and influence function. It is found that this new estimator provides reasonable protection against outliers while also being simple to compute. Using a Monte Carlo simulation, we compare the performance of the new estimator and several well-known methods, including the maximum likelihood, ordinary least-squares and weighted least-squares methods in the absence and presence of outliers. The results reveal that the new estimator is highly competitive with the maximum likelihood estimator in the absence of outliers and outperforms the other methods in the presence of outliers. Finally, we conduct a statistical analysis of four reliability data sets, the results of which support the simulation results.