Monte Carlo Estimation of g(μ) from Normally Distributed Data with Applications
نویسندگان
چکیده
We derive Monte Carlo-amenable solutions to the problem of unbiased estimation of a nonlinear function of the mean of a normal distribution. For most nonlinear functions the maximum likelihood estimator is biased. Our method yields a Monte Carlo approximation to the uniformly minimum variance unbiased estimator for a wide class of nonlinear functions. Applications to problems arising in the analysis of data measured with error and the secondary analysis of estimated data are described.
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