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Related Experiment Videos

Quantile maximum likelihood estimation of response time distributions.

Andrew Heathcote1, Scott Brown, D J K Mewhort

  • 1School of Behavioural Science, University of Newcastle, Callaghan, NSW, Australia. andrew.heathcote@newcastle.edu.au

Psychonomic Bulletin & Review
|July 18, 2002
PubMed
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A new quantile maximum likelihood (QML) estimation technique improves fitting of response time (RT) data distributions. QML is more efficient and less biased than existing methods for the ex-Gaussian distribution.

Area of Science:

  • Cognitive Psychology
  • Psychometrics
  • Computational Statistics

Background:

  • Response time (RT) data analysis often involves fitting distribution functions.
  • Existing estimation techniques may suffer from inefficiency and bias when fitting grouped RT data.
  • The ex-Gaussian distribution is commonly used for modeling RT data.

Purpose of the Study:

  • To introduce and evaluate a novel, robust estimation technique for grouped RT data.
  • To compare the performance of the new technique against existing methods.
  • To provide guidance for practical application and computational efficiency.

Main Methods:

  • A Monte Carlo simulation study was employed for evaluation.
  • The new technique, quantile maximum likelihood (QML), fits distribution functions to data grouped by sample quantiles.

Related Experiment Videos

  • Performance was assessed based on efficiency and bias compared to alternative methods.

    • Main Results:

    • The quantile maximum likelihood (QML) estimator demonstrated superior efficiency and reduced bias compared to the best alternative technique for the ex-Gaussian distribution.
    • Limitations of the Monte Carlo results were identified.
    • Guidance for practical implementation was provided.

    Conclusions:

    • QML offers a more robust and accurate method for estimating distribution functions from grouped RT data.
    • The developed open-source code facilitates the application of QML for various distribution functions.
    • Further research may explore QML's performance with different data types and distributions.