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    Cross-Validation in Statistical Climate Forecast Models

    Source: Journal of Climate and Applied Meteorology:;1987:;Volume( 026 ):;Issue: 011::page 1589
    Author:
    Michaelsen, Joel
    DOI: 10.1175/1520-0450(1987)026<1589:CVISCF>2.0.CO;2
    Publisher: American Meteorological Society
    Abstract: Cross-validation is a statistical procedure that produces an estimate of forecast skill which is less biased than the usual hindcast skill estimates. The cross-validation method systematically deletes one or more cases in a dataset, derives a forecast model from the remaining cases, and tests it on the deleted case or cases. The procedure is nonparametric and can be applied to any automated model building technique. It can also provide important diagnostic information about influential cases in the dataset and the stability of the model. Two experiments were conducted using cross-validation to estimate forecast skill in different predictive models of North Pacific sea surface temperatures (SSTs). The results indicate that bias, or artificial predictability (defined here as the difference between the usual hindcast skill and the forecast skill estimated by cross-validation), increases with each decision?either screening of potential predictors or fixing the value of a coefficient?drawn from the data. Bias introduced by variable screening depends on the size of the pool of potential predictors, while bias produced by fitting coefficients depends on the number of coefficients. The results also indicate that winter SSTs are predictable with a skill of about 20%?25%. Several models were compared. More flexible ones which allow the data to guide the selection of variables generally show poorer skill than the relatively inflexible models where a priori variable selection is used. The cross-validation estimates of artificial skill were compared with estimates derived from other methods. Davis and Chelton's method showed close agreement with the cross-validation results for a priori models. However, Monte Carlo estimates and cross-validation estimates do not agree well in the case of predictor screening models. The results of this study indicate that the amount of artificial skill depends on the amount of true skill, so Monte Carlo techniques which assume no true skill cannot be expected to perform well when there is some true skill.
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      Cross-Validation in Statistical Climate Forecast Models

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    contributor authorMichaelsen, Joel
    date accessioned2017-06-09T14:02:04Z
    date available2017-06-09T14:02:04Z
    date copyright1987/11/01
    date issued1987
    identifier issn0733-3021
    identifier otherams-11263.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4146472
    description abstractCross-validation is a statistical procedure that produces an estimate of forecast skill which is less biased than the usual hindcast skill estimates. The cross-validation method systematically deletes one or more cases in a dataset, derives a forecast model from the remaining cases, and tests it on the deleted case or cases. The procedure is nonparametric and can be applied to any automated model building technique. It can also provide important diagnostic information about influential cases in the dataset and the stability of the model. Two experiments were conducted using cross-validation to estimate forecast skill in different predictive models of North Pacific sea surface temperatures (SSTs). The results indicate that bias, or artificial predictability (defined here as the difference between the usual hindcast skill and the forecast skill estimated by cross-validation), increases with each decision?either screening of potential predictors or fixing the value of a coefficient?drawn from the data. Bias introduced by variable screening depends on the size of the pool of potential predictors, while bias produced by fitting coefficients depends on the number of coefficients. The results also indicate that winter SSTs are predictable with a skill of about 20%?25%. Several models were compared. More flexible ones which allow the data to guide the selection of variables generally show poorer skill than the relatively inflexible models where a priori variable selection is used. The cross-validation estimates of artificial skill were compared with estimates derived from other methods. Davis and Chelton's method showed close agreement with the cross-validation results for a priori models. However, Monte Carlo estimates and cross-validation estimates do not agree well in the case of predictor screening models. The results of this study indicate that the amount of artificial skill depends on the amount of true skill, so Monte Carlo techniques which assume no true skill cannot be expected to perform well when there is some true skill.
    publisherAmerican Meteorological Society
    titleCross-Validation in Statistical Climate Forecast Models
    typeJournal Paper
    journal volume26
    journal issue11
    journal titleJournal of Climate and Applied Meteorology
    identifier doi10.1175/1520-0450(1987)026<1589:CVISCF>2.0.CO;2
    journal fristpage1589
    journal lastpage1600
    treeJournal of Climate and Applied Meteorology:;1987:;Volume( 026 ):;Issue: 011
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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