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V. Brazauskas and J. Ghorai, “Estimating the Common Parameter of Normal Models with Known Coefficients of Variation: A Sensitivity Study of Asymptotically Efficient Estimators,” Journal of Statistical Computation and Simulation, Vol. 77, No. 8, 2007, pp. 663-681.
http://dx.doi.org/10.1080/10629360600578221

has been cited by the following article:

• TITLE: Inference for the Normal Mean with Known Coefficient of Variation

  AUTHORS: Yuejiao Fu, Hangjing Wang, Augustine Wong

  KEYWORDS: Canonical Parameter; Coverage Probability; Curved Exponential Family; Modified Signed Log Likelihood Ratio Statistic

  JOURNAL NAME: Open Journal of Statistics, Vol.3 No.6A, December 27, 2013

  ABSTRACT: Inference for the mean of a normal distribution with known coefficient of variation is of special theoretical interest be- cause the model belongs to the curved exponential family with a scalar parameter of interest and a two-dimensional minimal sufficient statistic. Therefore, standard inferential methods cannot be directly applied to this problem. It is also of practical interest because this problem arises naturally in many environmental and agriculture studies. In this paper we proposed a modified signed log likelihood ratio method to obtain inference for the normal mean with known coeffi- cient of variation. Simulation studies show the remarkable accuracy of the proposed method even for sample size as small as 2. Moreover, a new point estimator for the mean can be derived from the proposed method. Simulation studies show that new point estimator is more efficient than most of the existing estimators.