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| Registros recuperados : 2 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
06/05/2019 |
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Data da última atualização: |
06/05/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
BRIGHENTI, C. R. G.; CIRILLO, M. A.; COSTA, A. L. A.; ROSA, S. D. V. F. da; GUIMARÃES, R. M. |
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Afiliação: |
Carla Regina Guimarães Brighenti, Universidade Federal de São João Del-Rei/Departamento de Zootecnia; Marcelo Ângelo Cirillo, Universidade Federal de Lavras- UFLA/Departamento de Estatística; André Luís Alves Costa, Universidade Federal de Lavras- UFLA/Departamento de Estatística; STTELA DELLYZETE VEIGA F DA ROSA, CNPCa; Renato Mendes Guimarães, Universidade Federal de Lavras - UFLA/Deptaramento de Agricultura. |
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Título: |
Bayesian sequential procedure to estimate the viability of seeds Coffea arabica L. in tetrazolium test. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Scientia Agricola, v. 76, n. 3, p. 198-207, May/June. 2019 |
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Idioma: |
Inglês |
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Conteúdo: |
Tetrazolium tests use conventional sampling techniques in which a sample has a fixed size. These tests may be improved by sequential sampling, which does not work with fixedsize samples. When data obtained from an experiment are analyzed sequentially the analysis can be terminated when a particular decision has been made, and thus, there is no need to pre-establish the number of seeds to assess. Bayesian statistics can also help, if we have sufficient knowledge about coffee production in the area to construct a prior distribution. Therefore, we used the Bayesian sequential approach to estimate the percentage of viable coffee seeds submitted to tetrazolium testing, and we incorporated priors with information from other analyses of crops from previous years. We used the Beta prior distribution and, using data obtained from sample lots of Coffea arabica, determined its hyperparameters with a histogram and O?Hagan?s methods. To estimate the lowest risk, we computed the Bayes risks, which provided us with a basis for deciding whether or not we should continue the sampling process. The results confirm that the Bayesian sequential estimation can indeed be used for the tetrazolium test: the average percentage of viability obtained with the conventional frequentist method was 88 %, whereas that obtained with the Bayesian method with both priors was 89 %. However, the Bayesian method required, on average, only 89 samples to reach this value while the traditional estimation method needed as many as 200 samples. MenosTetrazolium tests use conventional sampling techniques in which a sample has a fixed size. These tests may be improved by sequential sampling, which does not work with fixedsize samples. When data obtained from an experiment are analyzed sequentially the analysis can be terminated when a particular decision has been made, and thus, there is no need to pre-establish the number of seeds to assess. Bayesian statistics can also help, if we have sufficient knowledge about coffee production in the area to construct a prior distribution. Therefore, we used the Bayesian sequential approach to estimate the percentage of viable coffee seeds submitted to tetrazolium testing, and we incorporated priors with information from other analyses of crops from previous years. We used the Beta prior distribution and, using data obtained from sample lots of Coffea arabica, determined its hyperparameters with a histogram and O?Hagan?s methods. To estimate the lowest risk, we computed the Bayes risks, which provided us with a basis for deciding whether or not we should continue the sampling process. The results confirm that the Bayesian sequential estimation can indeed be used for the tetrazolium test: the average percentage of viability obtained with the conventional frequentist method was 88 %, whereas that obtained with the Bayesian method with both priors was 89 %. However, the Bayesian method required, on average, only 89 samples to reach this value while the traditional estimation method need... Mostrar Tudo |
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Palavras-Chave: |
Beta distribution; Coffee; Prior distribution; Seed analysis. |
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Thesaurus NAL: |
Sampling. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/196980/1/Bayesian-sequential-procedure-to-estimate.pdf
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Marc: |
LEADER 02220naa a2200229 a 4500
001 2108760
005 2019-05-06
008 2019 bl uuuu u00u1 u #d
100 1 $aBRIGHENTI, C. R. G.
245 $aBayesian sequential procedure to estimate the viability of seeds Coffea arabica L. in tetrazolium test.$h[electronic resource]
260 $c2019
520 $aTetrazolium tests use conventional sampling techniques in which a sample has a fixed size. These tests may be improved by sequential sampling, which does not work with fixedsize samples. When data obtained from an experiment are analyzed sequentially the analysis can be terminated when a particular decision has been made, and thus, there is no need to pre-establish the number of seeds to assess. Bayesian statistics can also help, if we have sufficient knowledge about coffee production in the area to construct a prior distribution. Therefore, we used the Bayesian sequential approach to estimate the percentage of viable coffee seeds submitted to tetrazolium testing, and we incorporated priors with information from other analyses of crops from previous years. We used the Beta prior distribution and, using data obtained from sample lots of Coffea arabica, determined its hyperparameters with a histogram and O?Hagan?s methods. To estimate the lowest risk, we computed the Bayes risks, which provided us with a basis for deciding whether or not we should continue the sampling process. The results confirm that the Bayesian sequential estimation can indeed be used for the tetrazolium test: the average percentage of viability obtained with the conventional frequentist method was 88 %, whereas that obtained with the Bayesian method with both priors was 89 %. However, the Bayesian method required, on average, only 89 samples to reach this value while the traditional estimation method needed as many as 200 samples.
650 $aSampling
653 $aBeta distribution
653 $aCoffee
653 $aPrior distribution
653 $aSeed analysis
700 1 $aCIRILLO, M. A.
700 1 $aCOSTA, A. L. A.
700 1 $aROSA, S. D. V. F. da
700 1 $aGUIMARÃES, R. M.
773 $tScientia Agricola$gv. 76, n. 3, p. 198-207, May/June. 2019
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Embrapa Café (CNPCa) |
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