edu.ucar.opensky::articles:23996 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-25T06:42:16.472900 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d7bz69ct Bootstrap methods for statistical inference. Part I: Comparative forecast verification for continuous variables 2020-11-01T00:00:00Z publication https://n2t.org/ark:/85065/d7bz69ct https://doi.org/10.1175/JTECH-D-20-0069.1 Gilleland, Eric author UCAR/NCAR - Library publisher 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 two weather 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. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright 2020 American Meteorological Society (AMS). None eng geoscientificInformation