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- W1978976444 abstract "Providing certain parameters are known, almost any linear map from RP to R1 can be adjusted to yield a consistent and unbiased estimator in the context of estimating the mixing proportion θ on the basis of an unclassified sample of observations taken from a mixture of two p-dimensional distributions in proportions θ and 1-θ. Attention is focused on an estimator proposed recently, θ, which has minimum variance over all such linear maps. Unfortunately, the form of θ depends on the means of the component distributions and the covariance matrix of the mixture distribution. The effect of using appropriate sample estimates for these unknown parameters in forming θ is investigated by deriving the asymptotic mean and variance of the resulting estimator. The relative efficiency of this estimator under normality is derived. Also, a study is undertaken of the performance of a similar type of estimator appropriate in the context where an observed data vector is not an observation from either one or the other onent di..." @default.
- W1978976444 created "2016-06-24" @default.
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- W1978976444 date "1982-01-01" @default.
- W1978976444 modified "2023-09-25" @default.
- W1978976444 title "On the bias and variance of some proportion estimators" @default.
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- W1978976444 doi "https://doi.org/10.1080/03610918208812290" @default.
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