Multi-Stage Estimation Methodologies for an Inverse Gaussian Mean with Known Coefficient of Variation

Abstract In this paper, we develop accelerated sequential and stage procedures for estimating the mean of an inverse Gaussian distribution when the population coefficient of variation is known. The problems of minimum risk and bounded risk point estimation are handled. The estimation procedures are developed under an interesting weighted squared-error loss function and our aim is to control the associated risk functions. In spite of the usual estimator, i.e., the sample mean, Searls (1964) estimator is utilized for the purpose of estimation. Second-order asymptotics are obtained for the expected sample size and risk associated with the proposed multi-stage procedures. Further, it is established that the Searls’ estimator dominates the usual estimator (sample mean) under the proposed procedures. Extensive simulation analysis is carried out in support of the encouraging performances of the proposed methodologies and a real data example is also provided for illustrative purposes.