الفهرس 
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Abstract
Abstract
This thesis studies the censoring of bivariate models based on copula function in expand. Two bivariate distributions with different dependence measures have been discussed. The Farlie-Gumbel-Morgenstern bivariate exponentiated Weibull distribution (FGMBEW) and the Farlie-Gumbel-Morgenstern bivariate exponentiated Frechet distribution (FGMBEF) have been obtained. Its statistical properties such as marginal distributions, product moment, moment generation, reliability, hazard rate functions, and conditional distributions have been presented. Different goodness of fit measures for bivariate models have been explained as Kolmogorov-Smirnov statistic and Anderson-Darling. Progressive Type-II censoring of the bivariate models has evolved from fixed removal. To check these new criteria, we used bivariate exponentiated Weibull and bivariate exponentiated Frechet distributions. Two different methods for parameter estimation of the FGMBEW and FGMBEF distributions have been discussed. These methods are the maximum likelihood estimation (MLE) method and the inference functions for margins (IFM) method. Two different methods for computing the confidence interval for parameters have been discussed: asymptotic confidence intervals and bootstrap confidence intervals. Progressive Type-II censoring of the bivariate models has evolved from fixed removal by using the maximum likelihood estimation (MLE) method. Finally, parameter estimation for the FGMBEW and FGMBEF distributions under progressive Type-II censoring samples has been introduced. In addition, two real data sets have been introduced and analyzed to examine the model. Constant accelerated life testing for a bivariate model based on progressive censored samples with fixed Removal has been introduced. A simulation study has compared the preferences between sample size and progressive censoring schemes. The simulation results confirm that scheme III is the best censoring scheme for FGMBEW where it has the lowest Bias, MSE, and scheme II is the best censoring scheme for FGMBEF where it has the lowest Bias, MSE.