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Trigonometric Maximum Likelihood Estimation and Application to the Analysis of Incomplete Survival Information
Michael E. Tarter
Journal of the American Statistical Association
Vol. 74, No. 365 (Mar., 1979), pp. 132-139
Stable URL: http://www.jstor.org/stable/2286742
Page Count: 8
You can always find the topics here!Topics: Estimators, Fourier series, Statistical estimation, Statistics, Histograms, Fourier coefficients, Density estimation, Kaplan meiers estimate, Maximum likelihood estimators, Maximum likelihood estimation
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A procedure is derived for computing maximum likelihood estimates from the sample trigonometric moments. Several examples are given which demonstrate the convenience of this hybrid approach. The Zippin-Armitage method for dealing with incomplete survival information is generalized. A simple expression is obtained for the survival curve given nonparametric estimators of (1) the probability density of survival times for patients who have died, and (2) the probability density of known survival times for patients still alive. It is shown that this new estimator approaches the Kaplan-Meier product limit as the sample size approaches infinity.
Journal of the American Statistical Association © 1979 American Statistical Association