Reliability analysis is the key to evaluate software's quality. Since the early 1970s, the Power Law Process, among others, has been used to assess the rate of change of software reliability as time-varying function by using its intensity function. The Bayesian analysis applicability to the Power Law Process is justified using real software failure times. The choice of a loss function is an important entity of the Bayesian settings. The analytical estimate of likelihood-based Bayesian reliability estimates of the Power Law Process under the squared error and Higgins-Tsokos loss functions were obtained for different prior knowledge of its key parameter. As a result of a simulation analysis and using real data, the Bayesian reliability estimate under the Higgins-Tsokos loss function not only is robust as the Bayesian reliability estimate under the squared error loss function but also performed better, where both are superior to the maximum likelihood reliability estimate. A sensitivity analysis resulted in the Bayesian estimate of the reliability function being sensitive to the prior, whether parametric or non-parametric, and to the loss function. An interactive user interface application was additionally developed using Wolfram language to compute and visualize the Bayesian and maximum likelihood estimates of the intensity and reliability functions of the Power Law Process for a given data.
Global warming is majorly caused by an increase in atmospheric temperature and carbon dioxide (CO 2) emissions due to the rise in the temperature. The continued accumulation of CO 2 into the atmosphere is a massive part of the climate change problem. This study aims to develop a data-driven statistical model using Africa's fossil-fuel CO 2 emissions real data to identify the significant attributable variables and their interaction that produce the carbon dioxide emissions. However, we have considered five attributable variables in our statistical modeling and they are Liquid fuels (Li), Solid fuels (So), Gas fuels (Ga), Gas flares (Gf) and Cement production. The development of the statistical model that contains the different emissions of fossil fuels and their interactions have been specified and ranked based on a percentage of their annual contributions to carbon dioxide in the atmosphere. Our proposed statistical model is compared with a different penalization method since multicollinearity among the risk factors exists and which provided excellent results according to the root mean square errors (RMSE) statistic. The results of the proposed model are compared to previous results of different countries of the world.
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