PotvinRoff1993

Référence

Potvin, C., Roff, D.A. (1993) Distribution-free and robust statistical methods: viable alternatives to parametric statistics? Ecology, 74(6):1617-1628.

Résumé

After making a case for the prevalence of nonnormality, this paper introduces some distribution-free and robust techniques to ecologists and offers a critical appraisal of the potential advantages and drawbacks of these methods. The techniques presented fall into two distinct categories, methods based on ranks and "computer-intensive' techniques. Distribution-free rank tests free the practioner from concern about the underlying distribution and are very robust to outliers. If the distribution underlying the observations is other than normal, rank tests tend to be more efficient than their parametric counterparts. An entire body of novel distribution-free methods has been developed in parallel with the increasing capacities of today's computers to process large quantities of data. These techniques either reshuffle or resample a data set. The former are 'permutation' or "randomization' methods, the latter "bootstrap' techniques. -from Authors

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@ARTICLE { PotvinRoff1993,
    AUTHOR = { Potvin, C. and Roff, D.A. },
    TITLE = { Distribution-free and robust statistical methods: viable alternatives to parametric statistics? },
    JOURNAL = { Ecology },
    YEAR = { 1993 },
    VOLUME = { 74 },
    PAGES = { 1617-1628 },
    NUMBER = { 6 },
    NOTE = { 00129658 (ISSN) Cited By (since 1996): 205 Export Date: 26 April 2007 Source: Scopus Language of Original Document: English },
    ABSTRACT = { After making a case for the prevalence of nonnormality, this paper introduces some distribution-free and robust techniques to ecologists and offers a critical appraisal of the potential advantages and drawbacks of these methods. The techniques presented fall into two distinct categories, methods based on ranks and "computer-intensive' techniques. Distribution-free rank tests free the practioner from concern about the underlying distribution and are very robust to outliers. If the distribution underlying the observations is other than normal, rank tests tend to be more efficient than their parametric counterparts. An entire body of novel distribution-free methods has been developed in parallel with the increasing capacities of today's computers to process large quantities of data. These techniques either reshuffle or resample a data set. The former are 'permutation' or "randomization' methods, the latter "bootstrap' techniques. -from Authors },
    KEYWORDS = { distribution-free statistics robust statistical method },
    OWNER = { brugerolles },
    TIMESTAMP = { 2007.12.05 },
}

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