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    Bootstrap methods for statistical inference. Part I: Comparative forecast verification for continuous variables

    Source: Journal of Atmospheric and Oceanic Technology:;2020:;volume( ):;issue: -::page 1
    Author:
    Gilleland, Eric
    DOI: 10.1175/JTECH-D-20-0069.1
    Publisher: American Meteorological Society
    Abstract: When making statistical inferences, bootstrap resampling methods are often appealing because of less stringent assumptions about the distribution of the statistic(s) of interest. However, the procedures are not free of assumptions. This paper addresses a specific situation that occurs frequently in atmospheric sciences where the standard bootstrap is not appropriate; comparative forecast verification of continuous variables. In this setting, the question to be answered concerns which of twoweather or climate models is better in the sense of some type of average deviation from observations. The series to be compared are generally strongly dependent, which invalidates the most basic bootstrap technique. This paper also introduces new bootstrap code from the R package distillery that facilitates easy implementation of appropriate methods for paired-difference-of-means bootstrap procedures for dependent data.
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      Bootstrap methods for statistical inference. Part I: Comparative forecast verification for continuous variables

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    contributor authorGilleland, Eric
    date accessioned2022-01-30T18:09:59Z
    date available2022-01-30T18:09:59Z
    date copyright9/29/2020 12:00:00 AM
    date issued2020
    identifier issn0739-0572
    identifier otherjtechd200069.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4264598
    description abstractWhen making statistical inferences, bootstrap resampling methods are often appealing because of less stringent assumptions about the distribution of the statistic(s) of interest. However, the procedures are not free of assumptions. This paper addresses a specific situation that occurs frequently in atmospheric sciences where the standard bootstrap is not appropriate; comparative forecast verification of continuous variables. In this setting, the question to be answered concerns which of twoweather or climate models is better in the sense of some type of average deviation from observations. The series to be compared are generally strongly dependent, which invalidates the most basic bootstrap technique. This paper also introduces new bootstrap code from the R package distillery that facilitates easy implementation of appropriate methods for paired-difference-of-means bootstrap procedures for dependent data.
    publisherAmerican Meteorological Society
    titleBootstrap methods for statistical inference. Part I: Comparative forecast verification for continuous variables
    typeJournal Paper
    journal titleJournal of Atmospheric and Oceanic Technology
    identifier doi10.1175/JTECH-D-20-0069.1
    journal fristpage1
    journal lastpage55
    treeJournal of Atmospheric and Oceanic Technology:;2020:;volume( ):;issue: -
    contenttypeFulltext
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