Sequential analysis applied to testing the mean of an inverse gaussian distribution with known coefficient of variation

Joshi, S; Shah, Monal

doi:10.1080/03610929008830271

Cited by 8 publications

(1 citation statement)

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“…However, the above mentioned citations are solely based on the problems of fixed-sample size scenario and the case when sample size is not fixed in advance has been overlooked in the literature. Joshi and Shah (1990) have developed the sequential probability ratio test (SPRT) for testing the hypothesis for the mean of an IG distribution assuming the population CV to be known. Singh (1998) proposed the sequential procedure for estimating a normal mean when CV is known.…”

Section: Introductionmentioning

confidence: 99%

Sequential Estimation of an Inverse Gaussian Mean with Known Coefficient of Variation

2021

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This paper deals with developing sequential procedures for estimating the mean of an inverse Gaussian (IG) distribution when the population coefficient of variation (CV) is known. We considerthe minimum risk and bounded risk point estimation problems respectively. Moreover, we make use of a weighted squared-error loss function and aim to control the associated risk functions. Instead of the usual estimator, i.e., the sample mean, Searls J. Amer. Stat. Assoc. 50, 1225-1226(1964 estimator is utilized for the purpose of estimation. Second-order approximations are also obtained under both estimation set-ups. We establish that Searls' estimator dominates the usual estimator (sample mean) under proposed sequential sampling procedures. An extensive simulation analysis is carried out to validate the theoretical findings and real data illustrations are also provided to show the practical utility of our proposed sequential stopping strategies.

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Section: Introductionmentioning

confidence: 99%