To meet the desired standard, it is important to monitor and analyze different engineering processes to obtain the desired output. The bivariate distributions have received a significant amount of attention in recent years due to their ability to describe randomness of natural as well as artificial mechanisms. In this article, a bivariate model is constructed by compounding two independent asymmetric univariate distributions and by using the nesting approach to study the effect of each component on reliability for better understanding. Furthermore, the Bayes analysis of system availability is studied by considering prior parametric variations in the failure time and repair time distributions. Basic statistical characteristics of marginal distribution like mean median and quantile function are discussed. We used inverse Gamma prior to study its frequentist properties by conducting a Monte Carlo Markov Chain (MCMC) sampling scheme.

In this paper we proposed Bayes estimators for complete sample of the Modified Inverse Rayleigh (MIR) parameters which was introduced by Khan (2014). Different approximation methods with squared error loss function (SELF) have been used to develop the bayes estimators for the unknown parameters. The proposed estimators are compared with the corresponding maximum likelihood estimators by simulation study on the basis of mean square error (MSE). To illustrate the usefulness and goodness of fit of Modified Inverse Rayleigh distribution we considered two real data sets.

In recent years, the popularity of Smartphone social networking sites has exploded, however, in prior studies the link between users' values, satisfaction, and continued desire to use SNSs was viewed as having little attention. The objective of this research is to establish a model that explores users' value and satisfaction as key antecedents of continuance intention towards the usage of mobile SNSs. Data was collected through a paper-based survey from a university and 532 valid responses from students were used for analysis. To validate our proposed model, and test the hypothesis, the Partial least square technique has been applied through smart pls. The empirical results revealed that both Hedonic value and Utilitarian value were significant antecedents of intention to continuance. Moreover, satisfaction was also found important determinant of intention to continue. The findings of this study will be helpful for marketers, web designers, and researchers to understand the importance of users' value and satisfaction in the context of mobile SNSs. Research implications are presented and directions for future studies are also suggested at the end of the study.

This paper presents a new bivariate mixture Lindley power function (BMLPF) distribution that employs a conditional approach with non-identical asymmetric distributions, distinguishing itself by the incorporation of a functional shape parameter. Various structural properties of bivariate distribution are presented, including explicit marginals, cumulative distribution function (CDF), product moments, correlation coefficients, conditional densities, moment generating functions, conditional mean, and variances. The parameters of the proposed distribution are evaluated using the maximum likelihood estimation method. To assess the effectiveness of this estimation approach, an extensive simulation study is carried out. The analysis quantifies these point estimators with their standard errors, RMSE, LCL, and UCL. This research significantly contributes to the development and application of bivariate distributions particularly in modeling and analyzing various types of data.