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Why Write Statistical Software? The Case of Robust Statistical Methods

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  • Arnold Stromberg
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    Abstract

    Robust statistical methods are designed to work well when classical assumptions, typically normality and/or the lack of outliers, are violated. Almost everyone agrees on the value of robust statistical procedures. Nonetheless, after more than 40 years and thousands of papers, few robust methods were available in standard statistical software packages until very recently. This paper argues that one of the primary reasons for the lack of robust statistical methods in standard statistical software packages is the fact that few developers of statistical methods are willing to write user-friendly and readable software for the methods they develop, regardless of the usefulness of the method. Recent changes in academic statistics make it highly desirable for all developers of statistical methods to provide usable code for their statistical methods.

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    Article provided by American Statistical Association in its journal Journal of Statistical Software.

    Volume (Year): 10 ()
    Issue (Month): i05 ()
    Pages:

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    Handle: RePEc:jss:jstsof:10:i05

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    Cited by:
    1. Robert Finger, 2010. "Review of ‘Robustbase’ software for R," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(7), pages 1205-1210, November/.
    2. Valentin Todorov & Peter Filzmoser, . "An Object-Oriented Framework for Robust Multivariate Analysis," Journal of Statistical Software, American Statistical Association, vol. 32(i03).
    3. Micah Altman & Jeff Gill & Michael P. McDonald, . "accuracy: Tools for Accurate and Reliable Statistical Computing," Journal of Statistical Software, American Statistical Association, vol. 21(i01).
    4. Wang, Daxue, 2008. "Herd behavior towards the market index: Evidence from 21 financial markets," IESE Research Papers D/776, IESE Business School.

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