bayes estimator

This paper considers parameter estimations in Lomax distribution under progressive type-II censoring with random removals, assuming that the number of units removed at each failure time has a binomial distribution. The maximum likelihood estimators (MLEs) are derived using the expectation-maximization (EM) algorithm. The Bayes estimates of the parameters are obtained using both the squared error and the asymmetric loss functions based on the Lindley approximation. We compare the performance of our procedures using a simulation study and real data.

In this paper, Pareto distribution was studied using a standard Bayes estimator. A Pareto distribution of two parameters is investigated to find the approximation of (M.S.E) of the shape parameter by depending on Tyler series of two variables to propose a model mathematically.

In this paper the problem of hypothesis testing is considered as an estimation problem within a decision-theoretic framework for estimating the accuracy of the test. The usual p-value is an admissible estimator for the one-sided testing of the scale parameter under the squared error loss function in the Pareto distribution. In the presence of nuisance parameter for model, the generalized p-value is inadmissible. Even though the usual p-value and the generalized p-value are inadmissible estimators for the one-sided testing of the shape parameter, it is difficult to exhibit a better estimator than the usual p-value. For the two-sided testing, although the usual p-value is generally inadmissible, it is remained as an estimator for the two-sided testing of the shape parameter.