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• W2029687411 startingPage "1209" @default.
• W2029687411 abstract "Hierarchical (or multilevel) statistical models have become increasingly popular in psychology in the last few years. In this article, we consider the application of multilevel modeling to the ex-Gaussian, a popular model of response times. We compare single-level and hierarchical methods for estimation of the parameters of ex-Gaussian distributions. In addition, for each approach, we compare maximum likelihood estimation with Bayesian estimation. A set of simulations and analyses of parameter recovery show that although all methods perform adequately well, hierarchical methods are better able to recover the parameters of the ex-Gaussian, by reducing variability in the recovered parameters. At each level, little overall difference was observed between the maximum likelihood and Bayesian methods." @default.
• W2029687411 created "2016-06-24" @default.
• W2029687411 creator A5047673441 @default.
• W2029687411 creator A5077470909 @default.
• W2029687411 date "2008-12-01" @default.
• W2029687411 modified "2023-09-24" @default.
• W2029687411 title "Bayesian and maximum likelihood estimation of hierarchical response time models" @default.
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• W2029687411 doi "https://doi.org/10.3758/pbr.15.6.1209" @default.
• W2029687411 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/2601029" @default.
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