Papers by Aymen I . Rawshdeh
METRON, 2018
This paper presents a Bayesian approach, using differential evolution Markov chain method, to est... more This paper presents a Bayesian approach, using differential evolution Markov chain method, to estimate the parameters of the failure time distribution and its percentiles based on grouped and non-grouped degradation data. The observed failure times are modeled by linear degradation path model with random degradation rates follow log-logistic distribution. Two Monte Carlo simulation studies are conducted. The first one is devoted to assess the performance of the proposed method with respect to the mean squared error (MSE) for different values of the scale and shape parameters of the degradation model using small, moderate and large sample sizes. The proposed method performs better when applied to non-grouped data compared with grouped data. The second simulation study is conducted to compare the proposed log-logistic model with a Weibull degradation model. More importantly, the log-logistic model outperforms the Weibull model. The proposed methods are demonstrated by modeling real life times of laser devices.
Austrian Journal of Statistics, 2019
The main purpose of this paper is to perform linear regression analysis on a continuous aggregate... more The main purpose of this paper is to perform linear regression analysis on a continuous aggregate outcome from a Bayesian perspective using a Markov chain Monte Carlo algorithm (Gibbs sampling). In many situations, data are partially available due to privacy and confidentiality of the subjects in the sample. So, in this study, the vector of outcomes, Y, is realistically assumed to be missing and is partially available through summary statistics, sum(Y), aggregated over groups of subjects, while the covariate values, X, are availablefor all subjects in the sample. The results of the simulation study highlight both the efficiency of the regression parameter estimates and the predictive power of the proposed model compared with classicalmethods. The proposed approach is fully implemented in an example regarding systolic blood pressure for illustrative purposes.
This paper develops a Bayesian approach to estimate the stress-strength reliability, the probabil... more This paper develops a Bayesian approach to estimate the stress-strength reliability, the probability that one random variable exceeds another. The proposed methodology utilizes an initial guess of this reliability through an informative prior, which constitutes the cornerstone of the model. Emphasis lies on exponentially distributed data, but the proposed method is applicable in a wider range of models with similar form of stress-strength reliability. A Monte Carlo simulation study is conducted to compare the performance of the new estimators with both the Maximum Likelihood and the Shrinkage estimators. The comparison is conducted with respect to the Mean Squared Error (MSE) for different values of the rate parameters of the exponential distribution. The proposed method outperforms the two aforementioned alternative methods. A demonstration is conducted through analyzing a real data set.
This paper presents a Bayesian approach, using differential evolution Markov chain method, to est... more This paper presents a Bayesian approach, using differential evolution Markov chain method, to estimate the parameters of the failure time distribution and its percentiles based on grouped and non-grouped degradation data. The observed failure times are modeled by linear degradation path model with random degradation rates follow log-logistic distribution. Two Monte Carlo simulation studies are conducted. The first one is devoted to assess the performance of the proposed method with respect to the mean squared error (MSE) for different values of the scale and shape parameters of the degradation model using small, moderate and large sample sizes. The proposed method performs better when applied to non-grouped data compared with grouped data. The second simulation study is conducted to compare the proposed log-logistic model with a Weibull degradation model. More importantly, the log-logistic model outperforms the Weibull model. The proposed methods are demonstrated by modeling real life times of laser devices.
The main purpose of this paper is to perform linear regression analysis on a continuous aggregate... more The main purpose of this paper is to perform linear regression analysis on a continuous aggregate outcome from a Bayesian perspective using a Markov chain Monte Carlo algorithm (Gibbs sampling). In many situations, data are partially available due to privacy and confidentiality of the subjects in the sample. So, in this study, the vector of outcomes, Y, is realistically assumed to be missing and is partially available through summary statistics, sum (Y), aggregated over groups of subjects, while the covariate values, X, are available for all subjects in the sample. The results of the simulation study highlight both the efficiency of the regression parameter estimates and the predictive power of the proposed model compared with classical methods. The proposed approach is fully implemented in an example regarding systolic blood pressure for illustrative purposes.
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Papers by Aymen I . Rawshdeh