| DOI: | 10.1016/j.quascirev.2018.10.032
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| Scopus记录号: | 2-s2.0-85057841108
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| 论文题名: | Statistical modeling of rates and trends in Holocene relative sea level |
| 作者: | Ashe E.L.; Cahill N.; Hay C.; Khan N.S.; Kemp A.; Engelhart S.E.; Horton B.P.; Parnell A.C.; Kopp R.E.
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| 刊名: | Quaternary Science Reviews
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| ISSN: | 2773791
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| 出版年: | 2019
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| 卷: | 204 | | 起始页码: | 58
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| 结束页码: | 77
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| 语种: | 英语
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| 英文关键词: | Hierarchical statistical modeling
; RSL
; Sea level
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| Scopus关键词: | Geochronology
; Sea level
; Statistics
; Correlation structure
; Nonparametric approaches
; Spatial and temporal variability
; Spatio-temporal models
; Spatiotemporal variability
; Statistical framework
; Statistical modeling
; Statistical treatment
; Uncertainty analysis
; Holocene
; modeling
; sea level change
; spatial variation
; temporal variation
; trend analysis
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| 英文摘要: | Characterizing the spatio-temporal variability of relative sea level (RSL) and estimating local, regional, and global RSL trends requires statistical analysis of RSL data. Formal statistical treatments, needed to account for the spatially and temporally sparse distribution of data and for geochronological and elevational uncertainties, have advanced considerably over the last decade. Time-series models have adopted more flexible and physically-informed specifications with more rigorous quantification of uncertainties. Spatio-temporal models have evolved from simple regional averaging to frameworks that more richly represent the correlation structure of RSL across space and time. More complex statistical approaches enable rigorous quantification of spatial and temporal variability, the combination of geographically disparate data, and the separation of the RSL field into various components associated with different driving processes. We review the range of statistical modeling and analysis choices used in the literature, reformulating them for ease of comparison in a common hierarchical statistical framework. The hierarchical framework separates each model into different levels, clearly partitioning measurement and inferential uncertainty from process variability. Placing models in a hierarchical framework enables us to highlight both the similarities and differences among modeling and analysis choices. We illustrate the implications of some modeling and analysis choices currently used in the literature by comparing the results of their application to common datasets within a hierarchical framework. In light of the complex patterns of spatial and temporal variability exhibited by RSL, we recommend non-parametric approaches for modeling temporal and spatio-temporal RSL. © 2018 Elsevier Ltd |
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| 资源类型: | 期刊论文
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/117415
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| Appears in Collections: | 气候变化与战略
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| Recommended Citation: | |
Ashe E.L.,Cahill N.,Hay C.,et al. Statistical modeling of rates and trends in Holocene relative sea level[J]. Quaternary Science Reviews,2019-01-01,204
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