Bootstrap methods for statistical inference. Part I: Comparative forecast verification for continuous variablesSource: Journal of Atmospheric and Oceanic Technology:;2020:;volume( ):;issue: -::page 1Author:Gilleland, Eric
DOI: 10.1175/JTECH-D-20-0069.1Publisher: 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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contributor author | Gilleland, Eric | |
date accessioned | 2022-01-30T18:09:59Z | |
date available | 2022-01-30T18:09:59Z | |
date copyright | 9/29/2020 12:00:00 AM | |
date issued | 2020 | |
identifier issn | 0739-0572 | |
identifier other | jtechd200069.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4264598 | |
description 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. | |
publisher | American Meteorological Society | |
title | Bootstrap methods for statistical inference. Part I: Comparative forecast verification for continuous variables | |
type | Journal Paper | |
journal title | Journal of Atmospheric and Oceanic Technology | |
identifier doi | 10.1175/JTECH-D-20-0069.1 | |
journal fristpage | 1 | |
journal lastpage | 55 | |
tree | Journal of Atmospheric and Oceanic Technology:;2020:;volume( ):;issue: - | |
contenttype | Fulltext |