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Related Experiment Videos

QMLE: fast, robust, and efficient estimation of distribution functions based on quantiles.

Scott Brown1, Andrew Heathcote

  • 1Department of Cognitive Sciences, University of California, Irvine, California 92697-5100, USA. scottb@uci.edu

Behavior Research Methods, Instruments, & Computers : a Journal of the Psychonomic Society, Inc
|January 30, 2004
PubMed
Summary

Quantile maximum likelihood (QML) estimation offers robust parameter estimates for response time data, even with small sample sizes. This study introduces open-source code to simplify QML implementation and analysis.

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Area of Science:

  • Cognitive psychology
  • Computational statistics
  • Psychometrics

Background:

  • Quantile maximum likelihood (QML) is an estimation technique for distribution parameters.
  • It is particularly useful for response time data and small sample sizes (n=40).
  • Computational challenges have limited the widespread implementation of QML.

Purpose of the Study:

  • To provide open-source Fortran 90 code for calculating QML estimates.
  • To offer standard maximum likelihood estimates alongside QML.
  • To facilitate the use of QML in analyzing response time data.

Main Methods:

  • Development and release of open-source Fortran 90 software.
  • Implementation of algorithms for QML and standard maximum likelihood estimation.

Related Experiment Videos

  • Inclusion of features for standard error, parameter intercorrelation, and quantile-quantile plot construction.

    • Main Results:

    • QML parameter estimates are shown to be asymptotically unbiased and normally distributed.
    • The software provides asymptotically correct standard error and parameter intercorrelation estimates.
    • The code is parallelizable and adaptable for other distributions.

    Conclusions:

    • The provided software significantly reduces the computational difficulty of implementing QML.
    • This facilitates robust and efficient parameter estimation for response time data.
    • The open-source nature and adaptability promote broader application in statistical analysis.