this article examines statistical inference for  where and are independent but not identically distributed pareto of the first kind (pareto (i)) random variables with same scale parameter but different shape parameters. the maximum likelihood, uniformly minimum variance unbiased and bayes estimators with gamma prior are used for this purpose. simulation studies which compare the estimators are ...

There are some experiences that researcher come across quite number of time for very large networks in the initial samples such that they cannot finish the sampling procedure. Two solutions have been proposed and used by marine biologists which we discuss in this article: i) Adaptive cluster sampling based on order statistics with a stopping rule, ii) Restricted adaptive cluster sampling. Until...

‎In this paper‎, ‎we have dealt with the distribution theory of concomitants of order statistics arising from Farlie-Gumbel-Morgenstern bivariate Lomax distribution‎. ‎We have discussed the estimation of the parameters associated with the distribution of the variable Y of primary interest‎, ‎based on the ranked set sample defined by ordering the marginal observations...

In this paper, we develop fuzzy, possibilistic hypothesis tests for testing crisp hypotheses a distribution parameter from data. these tests, fuzzy statistics are used, which produced by the possibility of estimated parameter, constructed known confidence intervals. The results in much better agreement with than ones respective popular book on statistics, uses critical values. We also present a...

Inverse sampling design is generally considered to be appropriate technique when the population is divided into two subpopulations, one of which contains only few units. In this paper, we derive the Horvitz-Thompson estimator for the population mean under inverse sampling designs, where subpopulation sizes are known. We then introduce an alternative unbiased estimator, corresponding to post-st...

In most situations the best estimator of a function of the parameter exists, but sometimes it has a complex form and we cannot compute its variance explicitly. Therefore, a lower bound for the variance of an estimator is one of the fundamentals in the estimation theory, because it gives us an idea about the accuracy of an estimator. It is well-known in statistical inference that the Cram&eac...

Some examples of absurd uniformly minimum variance unbiased estimators are discussed. Two reasons, argued in the literature, for having such estimators are lack of enough information in the available data and property of unbiasedness. In this paper, accepting both of these views, we show that an appropriate choice of loss function using a general concept of unbiasedness leads to risk unb...