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    Evaluating Rank Histograms Using Decompositions of the Chi-Square Test Statistic

    Source: Monthly Weather Review:;2008:;volume( 136 ):;issue: 006::page 2133
    Author:
    Jolliffe, Ian T.
    ,
    Primo, Cristina
    DOI: 10.1175/2007MWR2219.1
    Publisher: American Meteorological Society
    Abstract: Rank histograms are often plotted to evaluate the forecasts produced by an ensemble forecasting system?an ideal rank histogram is ?flat? or uniform. It has been noted previously that the obvious test of ?flatness,? the well-known ?2 goodness-of-fit test, spreads its power thinly and hence is not good at detecting specific alternatives to flatness, such as bias or over- or underdispersion. Members of the Cramér?von Mises family of tests do much better in this respect. An alternative to using the Cramér?von Mises family is to decompose the ?2 test statistic into components that correspond to specific alternatives. This approach is described in the present paper. It is arguably easier to use and more flexible than the Cramér?von Mises family of tests, and does at least as well as it in detecting alternatives corresponding to bias and over- or underdispersion.
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      Evaluating Rank Histograms Using Decompositions of the Chi-Square Test Statistic

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4207668
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    contributor authorJolliffe, Ian T.
    contributor authorPrimo, Cristina
    date accessioned2017-06-09T16:21:16Z
    date available2017-06-09T16:21:16Z
    date copyright2008/06/01
    date issued2008
    identifier issn0027-0644
    identifier otherams-66342.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4207668
    description abstractRank histograms are often plotted to evaluate the forecasts produced by an ensemble forecasting system?an ideal rank histogram is ?flat? or uniform. It has been noted previously that the obvious test of ?flatness,? the well-known ?2 goodness-of-fit test, spreads its power thinly and hence is not good at detecting specific alternatives to flatness, such as bias or over- or underdispersion. Members of the Cramér?von Mises family of tests do much better in this respect. An alternative to using the Cramér?von Mises family is to decompose the ?2 test statistic into components that correspond to specific alternatives. This approach is described in the present paper. It is arguably easier to use and more flexible than the Cramér?von Mises family of tests, and does at least as well as it in detecting alternatives corresponding to bias and over- or underdispersion.
    publisherAmerican Meteorological Society
    titleEvaluating Rank Histograms Using Decompositions of the Chi-Square Test Statistic
    typeJournal Paper
    journal volume136
    journal issue6
    journal titleMonthly Weather Review
    identifier doi10.1175/2007MWR2219.1
    journal fristpage2133
    journal lastpage2139
    treeMonthly Weather Review:;2008:;volume( 136 ):;issue: 006
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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