A likelihood‐based comparison of temporal models for physical processes

DOI10.1002/sam.10113OpenAlexW2034609994WikidataQ104697166 ScholiaQ104697166MaRDI QIDQ4969766

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Publication date: 14 October 2020

Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/sam.10113

zbMATH Keywords

time seriesblocklength-selectionclimate model diagnosticsdeterministic model evaluationmoving-block bootstrap

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Related Items (1)

Identifying trends in the spatial errors of a regional climate model via clustering

Uses Software

• bootlib

Cites Work

• Unnamed Item
• Correction to “Automatic Block-Length Selection for the Dependent Bootstrap” by D. Politis and H. White
• Estimating the dimension of a model
• Resampling methods for dependent data
• Block length selection in the bootstrap for time series
• Workshop on statistical approaches for the evaluation of complex computer models
• On Measuring and Correcting the Effects of Data Mining and Model Selection
• Automatic Block-Length Selection for the Dependent Bootstrap
• On blocking rules for the bootstrap with dependent data
• The impact of bootstrap methods on time series analysis
• A new look at the statistical model identification

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