From Many to One: Consensus Inference in a MIP
Abstract A Model Intercomparison Project (MIP) consists of teams who estimate the same underlying quantity (e.g., temperature projections to the year 2070). A simple average of the ensemble of the teams' outputs gives a consensus estimate, but it does not recognize that some outputs are more va...
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| Format: | Article |
| Language: | English |
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Wiley
2022-07-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1029/2022GL098277 |
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| author | Noel Cressie Michael Bertolacci Andrew Zammit‐Mangion |
| author_facet | Noel Cressie Michael Bertolacci Andrew Zammit‐Mangion |
| author_sort | Noel Cressie |
| collection | DOAJ |
| description | Abstract A Model Intercomparison Project (MIP) consists of teams who estimate the same underlying quantity (e.g., temperature projections to the year 2070). A simple average of the ensemble of the teams' outputs gives a consensus estimate, but it does not recognize that some outputs are more variable than others. Statistical analysis of variance (ANOVA) models offer a way to obtain a weighted frequentist consensus estimate of outputs with a variance that is the smallest possible. Modulo dependence between MIP outputs, the ANOVA approach weights a team's output inversely proportional to its variance, from which optimally weighted estimates follow. ANOVA weights can also provide a prior distribution for Bayesian Model Averaging of the MIP outputs when external evaluation data are available. We use a MIP of carbon‐dioxide‐flux inversions to illustrate the ANOVA‐based weighting and subsequent frequentist consensus inferences. |
| format | Article |
| id | doaj-art-edf2a7d76ee44f00bac93399587a027e |
| institution | DOAJ |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-edf2a7d76ee44f00bac93399587a027e2025-08-20T03:14:13ZengWileyGeophysical Research Letters0094-82761944-80072022-07-014914n/an/a10.1029/2022GL098277From Many to One: Consensus Inference in a MIPNoel Cressie0Michael Bertolacci1Andrew Zammit‐Mangion2School of Mathematics and Applied Statistics University of Wollongong Wollongong NSW AustraliaSchool of Mathematics and Applied Statistics University of Wollongong Wollongong NSW AustraliaSchool of Mathematics and Applied Statistics University of Wollongong Wollongong NSW AustraliaAbstract A Model Intercomparison Project (MIP) consists of teams who estimate the same underlying quantity (e.g., temperature projections to the year 2070). A simple average of the ensemble of the teams' outputs gives a consensus estimate, but it does not recognize that some outputs are more variable than others. Statistical analysis of variance (ANOVA) models offer a way to obtain a weighted frequentist consensus estimate of outputs with a variance that is the smallest possible. Modulo dependence between MIP outputs, the ANOVA approach weights a team's output inversely proportional to its variance, from which optimally weighted estimates follow. ANOVA weights can also provide a prior distribution for Bayesian Model Averaging of the MIP outputs when external evaluation data are available. We use a MIP of carbon‐dioxide‐flux inversions to illustrate the ANOVA‐based weighting and subsequent frequentist consensus inferences.https://doi.org/10.1029/2022GL098277analysis of variance (ANOVA)Model Intercomparison Project (MIP)multi model ensemblestatistically optimal weightsSUPE‐ANOVA frameworkuncertainty quantification |
| spellingShingle | Noel Cressie Michael Bertolacci Andrew Zammit‐Mangion From Many to One: Consensus Inference in a MIP Geophysical Research Letters analysis of variance (ANOVA) Model Intercomparison Project (MIP) multi model ensemble statistically optimal weights SUPE‐ANOVA framework uncertainty quantification |
| title | From Many to One: Consensus Inference in a MIP |
| title_full | From Many to One: Consensus Inference in a MIP |
| title_fullStr | From Many to One: Consensus Inference in a MIP |
| title_full_unstemmed | From Many to One: Consensus Inference in a MIP |
| title_short | From Many to One: Consensus Inference in a MIP |
| title_sort | from many to one consensus inference in a mip |
| topic | analysis of variance (ANOVA) Model Intercomparison Project (MIP) multi model ensemble statistically optimal weights SUPE‐ANOVA framework uncertainty quantification |
| url | https://doi.org/10.1029/2022GL098277 |
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