Effect of Non-Normality on a Sequential Test for Mean

Bhattacharjee, G. P.; Nagendra, Y.

doi:10.2307/2334225

Cited by 5 publications

(1 citation statement)

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“…Early work on sensitivity to assumptions in sequential inference was conducted by Bhattacharjee and Nagendra (1964) in the context of the Wald sequential test for the mean, and by Bhattacharjee (1965), Blumenthal and Govindarajulu (1977) and Ramkaran (1983) for Stein's two stage sampling procedure. Jureckova and Sen (1996) devoted several chapters to the sensitivity of point and interval estimation methods in sequential statistical inference.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of normal-based triple sampling sequential point estimation to the normality assumption

Yousef

Kimber

Hamdy

2013

Journal of Statistical Planning and Inference

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This article discusses the sensitivity of the sequential normal-based triple sampling procedure for estimating the population mean to departures from normality. We assume that the underlying population has finite absolute sixth moment and find that asymptotically the behaviour of the estimator and of the sample size depend on the skewness and kurtosis of the underlying distribution when using a squared error loss function with linear sampling cost. These results enable the effects of non-normality easily to be assessed both qualitatively and quantitatively. We supplement our asymptotic results with a simulation experiment to study the performance of the estimator and the sample size in a range of conditions.

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