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Improved Approximations for Multilevel Models with Binary Responses
Harvey Goldstein and Jon Rasbash
Journal of the Royal Statistical Society. Series A (Statistics in Society)
Vol. 159, No. 3 (1996), pp. 505-513
Stable URL: http://www.jstor.org/stable/2983328
Page Count: 9
You can always find the topics here!Topics: Multilevel models, Statistical estimation, Simulations, Modeling, Datasets, Statistical variance, Maximum likelihood estimation, Approximation, Estimation bias, Coefficients
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This paper discusses the use of improved approximations for the estimation of generalized linear multilevel models where the response is a proportion. Simulation studies by Rodriguez and Goldman have shown that in extreme situations large biases can occur, most notably when the response is binary, the number of level 1 units per level 2 unit is small and the underlying random parameter values are large. An improved approximation is introduced which largely eliminates the biases in the situation described by Rodriguez and Goldman.
Journal of the Royal Statistical Society. Series A (Statistics in Society) © 1996 Royal Statistical Society