摘要：In many scientific and practical matters particulary in medicial study, sometimes the subject understudy is rare or, it is in a way that we know it tends to find a known destination. In these situations, the unknownparameter is can be explained by the estimation methods. In such conditions, we solve the problem by applyingsome restrictions in the ranging space of the unknown parameter. We sometimes encounter such problems instatistics. In other words, we have to estimate the parameter under study by considering and applying its constraintsand limitations. we try to obtain the minimax estimator of the unknown parameter of Negative BinomialsDistribution under Linex Loss Function by considering a restriction on the parameter space. According to theaforementioned constraints and conditions, we should consider a two-point a priori distribution for the parameterunder study in order to make a Bayes' equalizer decision. Since we also want the Bayes' estimator to be minimax,the estimator should hold with some conditions. Given such conditions, Bayes' estimators and the minimax willhave a simple form. The consideration of these conditions will pose limitation also on the parameter under study,with Bayes' estimators being minimax for some values of the parameter space
关键词：Negative Binomial Distribution (NBD); Linex Loss Function (LLF); Restricted Parameter Space (RPS);Equalizer
