Estimation based on progressive first-failure censoring from exponentiated exponential distribution

In this paper, point and interval estimations for the parameters of the exponentiated exponential (EE) distribution are studied based on progressive first-failure-censored data. The Bayes estimates are computed based on squared error and Linex loss functions and using Markov Chain Monte Carlo (MCMC) algorithm. Also, based on this censoring scheme, approximate confidence intervals for the parameters of EE distribution are developed. Monte Carlo simulation study is carried out to compare the performances of the different methods by computing the estimated risks (ERs), as well as Akaike's information criteria (AIC) and Bayesian information criteria (BIC) of the estimates. Finally, a real data set is introduced and analyzed using EE and Weibull distributions. A comparison is carried out between the mentioned models based on the corresponding Kolmogorov–Smirnov (K–S) test statistic to emphasize that the EE model fits the data with the same efficiency as the other model. Point and interval estimation of all parameters are studied based on this real data set as illustrative example.     

Research and Identity: Role-efficacy,Benchmarking, and the Identity Construction Process

Abstract

In the spring of 2016, university students with little to no research experience volunteered to assist with surveying for a larger project on college success. Volunteers discussed their field experiences through written statements and semistructured interviews. Eighty log sheets and 14 in-depth interviews serve as the qualitative data for this study. We followed Braun and Clarke’s (2006) Thematic Analysis approach and reviewed the log sheets for key themes. Based on the initial analysis, semistructured standpoint interviews with student volunteers explored the development of a nascent research-based identity. Findings suggest that students’ research-based identity construction was a process that moved from low role-efficacy to emerging role-efficacy through the use of benchmarking and justification strategies that helped overcome negative emotions surrounding an initial sense of low role-efficacy with survey research tasks. We discuss how identity construction unfolded over time and suggest implications for this research.     

The twin deficit hypothesis in Egypt

We employ a new approach to the twin deficit hypothesis aimed at enhancing policy making in Egypt. In contrast to the conventional twin deficit hypothesis between the current account, which comprises many items out of governments’ scope of maneuvering, and the budget deficit, we track the causal link between Egypt’s merchandise trade deficit and the budget deficit. We begin first by examining the conventional twin deficit hypothesis using a VAR model, which implies short run reverse causation running from the current account deficit to the budget deficit. Second, as cointegration exists between the budget deficit and the merchandise trade deficit, we run a multivariate VECM model which refutes the twin deficit hypothesis in favor of the current account targeting hypothesis. In policy terms, ameliorating Egypt’s trade balance would ultimately improve its fiscal balance as well.     

Finite mixture of Burr type XII distribution and its reciprocal: properties and applications

In this paper, the finite mixture of Burr type XII distribution with its reciprocal, is proposed as a failure model. The failure rate (FR) of the new model covers several types of failure rates. It is shown that depending on the parameter values, the model is capable of covering different combinations of failure rates. A study of the behavior of the FR curve of the model is made.     

Bayesian prediction based on type-I censored data from a mixture of Burr type XII distribution and its reciprocal

This paper is concerned with the problem of obtaining Bayesian prediction bounds of future observables from a finite mixture of Burr type XII distribution with its reciprocal based on type-I censored data. We consider the one-sample and two-sample prediction schemes using the Markov chain Monte Carlo algorithm. Numerical examples are given to illustrate the procedures and the accuracy of prediction intervals is investigated via extensive Monte Carlo simulation.