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Nonparametric Maximum Likelihood Estimation of a Mixing Distribution

^aHarvard University
^bDepartment of Statistics, Department of Biostatistics , Harvard University , Cambridge , MA , 02138 , USA
✉Corresponding author

Journal of the American Statistical Association1978-12-01 860 citations33 references

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Abstract

The nonparametric maximum likelihood estimate of a mixing distribution is shown to be self-consistent, a property which characterizes the nonparametric maximum likelihood estimate of a distribution function in incomplete data problems. Under various conditions the estimate is a step function, with a finite number of steps. Its computation is illustrated with a small example.

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Nonparametric Maximum Likelihood Estimation of a Mixing Distribution

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