6533b821fe1ef96bd127b819
RESEARCH PRODUCT
Corrigendum: ExGUtils: A Python Package for Statistical Analysis With the ex-Gaussian Probability Density
Carmen Moret-tatayDaniel GamermannEsperanza Navarro-pardoPedro Fernández De Córdoba Castellásubject
FOS: Computer and information sciencesResponse timeslcsh:BF1-990Probability density functionex-Gaussian fitStatistics - Applications050105 experimental psychology03 medical and health sciences0302 clinical medicineSignificance testingresponse componentsConceptual AnalysisPsychology0501 psychology and cognitive sciencesStatistical analysisApplications (stat.AP)Ex-Gaussian fitTempo de reaçãoGeneral Psychologycomputer.programming_languagesignificance testingResponse componentsNumerical analysis05 social sciencesAnálise estatísticaCorrectionPython (programming language)Ex gaussianDistribuição Gaussianapythonlcsh:PsychologyOutlierTrimmingPsychologyMATEMATICA APLICADAAlgorithmcomputerSignificance testing030217 neurology & neurosurgeryresponse timesPythondescription
The study of reaction times and their underlying cognitive processes is an important field in Psychology. Reaction times are usually modeled through the ex-Gaussian distribution, because it provides a good fit to multiple empirical data. The complexity of this distribution makes the use of computational tools an essential element in the field. Therefore, there is a strong need for efficient and versatile computational tools for the research in this area. In this manuscript we discuss some mathematical details of the ex-Gaussian distribution and apply the ExGUtils package, a set of functions and numerical tools, programmed for python, developed for numerical analysis of data involving the ex-Gaussian probability density. In order to validate the package, we present an extensive analysis of fits obtained with it, discuss advantages and differences between the least squares and maximum likelihood methods and quantitatively evaluate the goodness of the obtained fits (which is usually an overlooked point in most literature in the area). The analysis done allows one to identify outliers in the empirical datasets and criteriously determine if there is a need for data trimming and at which points it should be done.
year | journal | country | edition | language |
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2018-05-01 |