Inference for Normal Mean with Known Coefficient of Variation
Comparison using Simulation and Real Examples
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Many of the phenomena observed in biological, physical and other natural sciences follow normal distribution. In the past few years, the primary interest was the inference concerning mean and variance of the observations, especially those which were related to the biological measurements. In such experimental study the past data provides information on the coefficient of variation (c.v.). When the data is analyzed for the final trials, past information on the variability in the yield can be made use of, for the estimation of the mean. The c.v. is a stable measure of dispersion and thus does no...
Many of the phenomena observed in biological, physical and other natural sciences follow normal distribution. In the past few years, the primary interest was the inference concerning mean and variance of the observations, especially those which were related to the biological measurements. In such experimental study the past data provides information on the coefficient of variation (c.v.). When the data is analyzed for the final trials, past information on the variability in the yield can be made use of, for the estimation of the mean. The c.v. is a stable measure of dispersion and thus does not change quite rapidly over the years or between the locations. The focus of this book is the inference concerning normal mean with known c.v. The performance of the estimators proposed in the literature are extensively studied and new tests are proposed for the mean. The performances of the tests are studied through simulations and through higher order asymptotics by deriving Edgeworth expansion for the null distribution of the test statistics.
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