Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2017WR021902 |
A Primer for Model Selection: The Decisive Role of Model Complexity | |
Hoege, Marvin1,2; Woehling, Thomas3,4; Nowak, Wolfgang1 | |
2018-03-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2018 |
卷号 | 54期号:3页码:1688-1715 |
文章类型 | Article |
语种 | 英语 |
国家 | Germany; New Zealand |
英文摘要 | Selecting a "best" model among several competing candidate models poses an often encountered problem in water resources modeling (and other disciplines which employ models). For a modeler, the best model fulfills a certain purpose best (e.g., flood prediction), which is typically assessed by comparing model simulations to data (e.g., stream flow). Model selection methods find the "best" trade-off between good fit with data and model complexity. In this context, the interpretations of model complexity implied by different model selection methods are crucial, because they represent different underlying goals of modeling. Over the last decades, numerous model selection criteria have been proposed, but modelers who primarily want to apply a model selection criterion often face a lack of guidance for choosing the right criterion that matches their goal. We propose a classification scheme for model selection criteria that helps to find the right criterion for a specific goal, i.e., which employs the correct complexity interpretation. We identify four model selection classes which seek to achieve high predictive density, low predictive error, high model probability, or shortest compression of data. These goals can be achieved by following either nonconsistent or consistent model selection and by either incorporating a Bayesian parameter prior or not. We allocate commonly used criteria to these four classes, analyze how they represent model complexity and what this means for the model selection task. Finally, we provide guidance on choosing the right type of criteria for specific model selection tasks. (A quick guide through all key points is given at the end of the introduction.) |
英文关键词 | model selection model complexity information criteria (IC) primer |
领域 | 资源环境 |
收录类别 | SCI-E ; SSCI |
WOS记录号 | WOS:000430364900016 |
WOS关键词 | BAYESIAN INFORMATION CRITERION ; CROSS-VALIDATION ; ASYMPTOTIC EQUIVALENCE ; ERROR RATE ; WATER ; LIKELIHOOD ; INFERENCE ; CHOICE ; PERFORMANCE ; PARAMETERS |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/21488 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Stuttgart, Inst Modelling Hydraul & Environm Syst LS3, SimTech, Stuttgart, Germany; 2.Univ Tubingen, Ctr Appl Geosci, Tubingen, Germany; 3.Tech Univ Dresden, Dept Hydrol, Dresden, Germany; 4.Lincoln Agritech, Lincoln Environm Res, Hamilton, New Zealand |
推荐引用方式 GB/T 7714 | Hoege, Marvin,Woehling, Thomas,Nowak, Wolfgang. A Primer for Model Selection: The Decisive Role of Model Complexity[J]. WATER RESOURCES RESEARCH,2018,54(3):1688-1715. |
APA | Hoege, Marvin,Woehling, Thomas,&Nowak, Wolfgang.(2018).A Primer for Model Selection: The Decisive Role of Model Complexity.WATER RESOURCES RESEARCH,54(3),1688-1715. |
MLA | Hoege, Marvin,et al."A Primer for Model Selection: The Decisive Role of Model Complexity".WATER RESOURCES RESEARCH 54.3(2018):1688-1715. |
条目包含的文件 | 条目无相关文件。 |
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