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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/338

Title: Obtaining the maximum likelihood estimates in incomplete R x C contingency tables using a Poisson generalized linear model
Authors: Lipsitz, Stuart R.
Parzen, Michael
Molenberghs, Geert
Keywords: Categorical data
Missing data
Issue Date: 1998
Citation: Journal of Computational and Graphical Statistics, 7(3). p. 356-376
Abstract: This article describes estimation of the cell probabilities in an R x C contingency table with ignorable missing data. Popular methods for maximizing the incomplete data likelihood are the EM-algorithm and the Newton--Raphson algorithm. Both of these methods require some modification of existing statistical software to get the MLEs of the cell probabilities as well as the variance estimates. We make the connection between the multinomial and Poisson likelihoods to show that the MLEs can be obtained in any generalized linear models program without additional programming or iteration loops.
URI: http://hdl.handle.net/1942/338
DOI: 10.2307/1390709
ISI #: 000076008800007
Type: Journal Contribution
Validation: ecoom, 1999
Appears in Collections: Research publications

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