QMLE: Fast, robust, and efficient estimation of distribution functions based on quantiles. Quantile maximum likelihood (QML) is an estimation technique, proposed by Heathcote, Brown, and Mewhort (2002), that provides robust and efficient estimates of distribution parameters, typically for response time data, in sample sizes as small as 40 observations. In view of the computational difficulty inherent in implementing QML, we provide open-source Fortran 90 code that calculates QML estimates for parameters of the ex-Gaussian distribution, as well as standard maximum likelihood estimates. We show that parameter estimates from QML are asymptotically unbiased and normally distributed. Our software provides asymptotically correct standard error and parameter intercorrelation estimates, as well as producing the outputs required for constructing quantile—quantile plots. The code is parallelizable and can easily be modified to estimate parameters from other distributions. Compiled binaries, as well as the source code, example analysis files, and a detailed manual, are available for free on the Internet.
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References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Grasman, Raoul P.P.P.; Wagenmakers, Eric-Jan; van der Maas, Han L.J.: On the mean and variance of response times under the diffusion model with an application to parameter estimation (2009)
- Ekström, Magnus: Alternatives to maximum likelihood estimation based on spacings and the Kullback-Leibler divergence (2008)