Abstract
SUMMARY 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. Keywortis: �BINARY RESPONSE; GENERALIZED LINEAR MODEL; HIERARCHICAL DATA; MARGINAL MODEL; MULTILEVEL MODEL; QUASI-LIKELIHOOD; UNIT-SPECIFIC MODEL
Keywords
Generalized linear mixed modelMultilevel modelGeneralized linear modelBinary numberMarginal modelHierarchical generalized linear modelMathematicsUnit (ring theory)Binary dataApplied mathematicsRandom effects modelLinear modelStatisticsHierarchical database modelComputer scienceRegression analysisData mining
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Publication Info
- Year
- 1996
- Type
- article
- Volume
- 159
- Issue
- 3
- Pages
- 505-505
- Citations
- 493
- Access
- Closed
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Cite This
Harvey Goldstein,
Jon Rasbash
(1996).
Improved Approximations for Multilevel Models with Binary Responses.
Journal of the Royal Statistical Society Series A (Statistics in Society)
, 159
(3)
, 505-505.
https://doi.org/10.2307/2983328
Identifiers
- DOI
- 10.2307/2983328