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The Empirical Distribution Function with Arbitrarily Grouped, Censored and Truncated Data
Journal of the Royal Statistical Society Series B (Statistical Methodology) · 1976 · Vol. 38(3) · pp. 290–295
Bruce W. Turnbull✉(University of Oxford)
Abstract
Summary This paper is concerned with the non-parametric estimation of a distribution function F, when the data are incomplete due to grouping, censoring and/or truncation. Using the idea of self-consistency, a simple algorithm is constructed and shown to converge monotonically to yield a maximum likelihood estimate of F. An application to hypothesis testing is indicated.
Statistical Methods and Bayesian InferenceStatistical Distribution Estimation and ApplicationsAdvanced Statistical Methods and ModelsStatisticsEmpirical distribution functionEconometricsDistribution (mathematics)Function (biology)MathematicsBiologyMathematical analysis
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References
Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm
Journal of the Royal Statistical Society Series B (Statistical Methodology) · 1977 · 49,286 citations
Nonparametric Estimation from Incomplete Observations
Journal of the American Statistical Association · 1958 · 38,793 citations
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