Within the context of clinical and other scientific research, a substantial need exists for an accurate determination of the point\nestimate in a lognormal mean model, given that highly skewed data are often present. As such, logarithmic transformations are\noften advocated to achieve the assumptions of parametric statistical inference. Despite this, existing approaches that utilize only a\nsample�s mean and variance may not necessarily yield the most efficient estimator. The current investigation developed and tested\nan improved efficient point estimator for a lognormal mean by capturing more complete information via the sample�s coefficient of\nvariation. Results of an empirical simulation study across varying sample sizes and population standard deviations indicated relative\nimprovements in efficiency of up to 129.47 percent compared to the usual maximum likelihood estimator and up to 21.33 absolute\npercentage points above the efficient estimator presented by Shen and colleagues (2006). The relative efficiency of the proposed\nestimator increased particularly as a function of decreasing sample size and increasing population standard deviation.
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