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| 1. |  | BATISTA, Â. G.; MENDONÇA, M. C. P.; SOARES, E. S.; MAIA, J. K. DA S.; DIONISIO, A. P.; SARTORI, C. R.; HÖFLING M. A. DA C.; MARÓSTICA JÚNIOR, M. R. Syzygium malaccense fruit supplementation protects mice brain against high-fat diet impairment and improves cognitive functions. Journal of Functional Foods, Amsterdam, v. 65, art. no. 103745, 2020.| Biblioteca(s): Embrapa Agroindústria Tropical. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Meio Ambiente. Para informações adicionais entre em contato com cnpma.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
11/01/2008 |
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Data da última atualização: |
24/03/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
Internacional - A |
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Autoria: |
MAIA, A. H. N.; MEINKE, H.; LENNOX, S.; STONE, R. |
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Afiliação: |
Aline de Holanda Nunes Maia, Embrapa Meio Ambiente; Holger Meinke, Queensland Departament of Primary Industries and Fisheries; Sarah Lennox, Queensland Departament of Primary Industries and Fisheries; Roger Stone, Queensland Departament of Primary Industries and Fisheries. |
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Título: |
Inferential, nonparametric statistics to assess the quality of probabilistic forecast systems. |
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Ano de publicação: |
2007 |
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Fonte/Imprenta: |
Monthly Weather Review, Boston, v. 135, n. 2, p. 351-362, 2007. |
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Idioma: |
Inglês |
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Conteúdo: |
Many statistical forecast systems are available to interested users. To be useful for decision making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and its statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of ?quality.? However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what quality entails and how to measure it, leading to confusion and misinformation. A generic framework is presented that quantifies aspects of forecast quality using an inferential approach to calculate nominal significance levels (p values), which can be obtained either by directly applying nonparametric statistical tests such as Kruskal?Wallis (KW) or Kolmogorov?Smirnov (KS) or by using Monte Carlo methods (in the case of forecast skill scores). Once converted to p values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. The analysis demonstrates the importance of providing p values rather than adopting some arbitrarily chosen significance levels such as 0.05 or 0.01, which is still common practice. This is illustrated by applying nonparametric tests (such as KW and KS) and skill scoring methods [linear error in the probability space (LEPS) and ranked probability skill score (RPSS)] to the five-phase Southern Oscillation index classification system using historical rainfall data from Australia, South Africa, and India. The selection of quality measures is solely based on their common use and does not constitute endorsement. It is found that nonparametric statistical tests can be adequate proxies for skill measures such as LEPS or RPSS. The framework can be implemented anywhere, regardless of dataset, forecast system, or quality measure. Eventually such inferential evidence should be complemented by descriptive statistical methods in order to fully assist in operational risk management. MenosMany statistical forecast systems are available to interested users. To be useful for decision making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and its statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of ?quality.? However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what quality entails and how to measure it, leading to confusion and misinformation. A generic framework is presented that quantifies aspects of forecast quality using an inferential approach to calculate nominal significance levels (p values), which can be obtained either by directly applying nonparametric statistical tests such as Kruskal?Wallis (KW) or Kolmogorov?Smirnov (KS) or by using Monte Carlo methods (in the case of forecast skill scores). Once converted to p values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. The analysis demonstrates the importance of providing p values rather than adopting some arbitrarily chosen significance levels such as 0.05 or 0.01, which is still common practice. This is illustrated by applying nonparametric tests (such as KW and KS) and skill scoring methods [linea... Mostrar Tudo |
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Palavras-Chave: |
Inferência; Modelo espacial. |
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Thesagro: |
Análise Estatística; Estatística; Modelo Matemático; Previsão do Tempo; Qualidade. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02904naa a2200241 a 4500
001 1015905
005 2023-03-24
008 2007 bl uuuu u00u1 u #d
100 1 $aMAIA, A. H. N.
245 $aInferential, nonparametric statistics to assess the quality of probabilistic forecast systems.$h[electronic resource]
260 $c2007
520 $aMany statistical forecast systems are available to interested users. To be useful for decision making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and its statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of ?quality.? However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what quality entails and how to measure it, leading to confusion and misinformation. A generic framework is presented that quantifies aspects of forecast quality using an inferential approach to calculate nominal significance levels (p values), which can be obtained either by directly applying nonparametric statistical tests such as Kruskal?Wallis (KW) or Kolmogorov?Smirnov (KS) or by using Monte Carlo methods (in the case of forecast skill scores). Once converted to p values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. The analysis demonstrates the importance of providing p values rather than adopting some arbitrarily chosen significance levels such as 0.05 or 0.01, which is still common practice. This is illustrated by applying nonparametric tests (such as KW and KS) and skill scoring methods [linear error in the probability space (LEPS) and ranked probability skill score (RPSS)] to the five-phase Southern Oscillation index classification system using historical rainfall data from Australia, South Africa, and India. The selection of quality measures is solely based on their common use and does not constitute endorsement. It is found that nonparametric statistical tests can be adequate proxies for skill measures such as LEPS or RPSS. The framework can be implemented anywhere, regardless of dataset, forecast system, or quality measure. Eventually such inferential evidence should be complemented by descriptive statistical methods in order to fully assist in operational risk management.
650 $aAnálise Estatística
650 $aEstatística
650 $aModelo Matemático
650 $aPrevisão do Tempo
650 $aQualidade
653 $aInferência
653 $aModelo espacial
700 1 $aMEINKE, H.
700 1 $aLENNOX, S.
700 1 $aSTONE, R.
773 $tMonthly Weather Review, Boston$gv. 135, n. 2, p. 351-362, 2007.
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