Improved estimation procedures for multilevel models with binary response: a case‐study

By G. Rodríguez, N. Goldman. 2001

228 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f4da51f661b8982be78daf970e8eb46cffb0cb8b

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Citations in the corpus

Regression Analysis of Country Effects Using Multilevel Data: A Cautionary Tale (2013, methodology): “We have used adaptive Gaussian quadrature, which has been found to produce more accurate estimates than penalized quasi-likelihood (Rodriguez and Goldman 2001, Pinheiro and Chao 2006, Austin 2010).”
ASREML user guide release 1.0 (2002, background): “For grouped binary data with small group sizes, estimation biases can be over 50% (e.g. Breslow and Lin, 1995, Goldstein and Rasbash, 1996, Rodriguez and Goldman, 2001, Waddington et al., 1994).”
Multilevel modelling of complex survey data (2006, methodology): “Unfortunately, this method is not expected to perform well since unweighted penalized quasi-likelihood often produces biased estimates, in particular when the responses are dichotomous (e.g. Rodríguez and Goldman (1995, 2001)).”
The Impact of Sample Size and Other Factors When Estimating Multilevel Logistic Models (2016, methodology): “These methods have been shown to yield biased point and standard error estimates (Breslow, 2005; Goldstein, 2003; Guo & Zhao, 2000; Kim, Choi, & Emery, 2013; Rodriguez & Goldman, 2001).”
Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects (2005, background): “Unfortunately, the parameter estimates from PQL tend to be biased for binary dependent variables with small cluster sizes and high intraclass correlations (e.g. Rodriguez and Goldman, 1995, 2001).”
Comparing parent–offspring regression with frequentist and Bayesian animal models to estimate heritability in wild populations: a simulation study for Gaussian and binary traits (2013, methodology): “Therefore, we emphasize that a method with a limited inacccuracy but rather high precision, such as MCMC for binary traits, or even PQL if the expected heritability is small enough (less than 0.1), should be preferred over a more accurate but highly imprecise method, such as the REMLc.”
Multilevel Modelling of Country Effects: A Cautionary Tale (2016, methodology): “This has been found to produce more accurate estimates than penalized quasi-likelihood both with data structures different from ours (Rodriguez and Goldman, 2001; Callens and Croux, 2005; Pinheiro and Chao, 2006; Austin, 2010) and also with the same data structure as used in this article (Jenkins, 2”