title: QMLE: fast, robust, and efficient estimation of distribution functions based on quantiles.
abstract: 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.
authors.affiliation: Department of Cognitive Sciences, University of California, Irvine, California 92697-5100, USA. scottb@uci.edu
authors.forename: Scott
authors.initials: S
authors.lastname: Brown
authors.forename: Andrew
authors.initials: A
authors.lastname: Heathcote
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D016011:Normal Distribution; D011381:Programming Languages; D011589:Psychology, Experimental / Q000379:methods*; D012984:Software