Estimate Of Parameter Of Morgenstern Type Bivariate Exponential Distribution With Moving Extreme Ranked Set Sampling

In many cases, ranked set sampling is a more efficient way of data collection than simple random sampling, and contains more information than simple random sampling.Therefore, from the last century, the ranked set sampling is used in statistical infer-ence. But in order to reduce the ranked error rate, Al-saleh proposed a modified set of samples, namely: moving extreme ranked set sampling, and in the case of a large number of ranked errors, In this sampling , we study the properties of the maximum likelihood estimation of the parameter A of the exponential distribution and give the form of unbiased estirmation of the modified maximum likelihood estimate of ?. In 2007, Al-saleh The problem of parameter estimation of two-dimensional normal distri-bution under dynamic extreme sort set sampling is studied.On the basis of the above research, this paper discusses the parameter estimation problem of Morgenstern type two-dimensional exponential distribution by using the method of dynamic extreme sort set sampling. Chacko studied the parameter estimation problem of Morgenstern type bivariate exponential distribution under ranked set sampling.In this paper, we mainly study the existence of the Morgenstern type bivariate ex-ponential distribution in the case of moving extreme ranked set sampling. We estimate the parameter ? of the variable Y in the case of the parameter ? of the auxiliary vari-able X. The first part introduces the previous research results and details the sampling method of RSS and MERSS.The second part is the preparation work, which mainly introduces the density function of the two-dimensional exponential distribution of Mor-gensternY is given the mean and variance of the order dependency variable when the auxiliary variable X is known, and the mean of the study variable Y is the unbiased estimate of its parameter ?. The MLE estimate and its properties of the parameter? in the case of 6 are known, and the maximum likelihood of the Fisher information by the study parameter AEstimates are better and prove that the A correction MLE is unbiased.