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Doctoral Thesis
DOI
https://doi.org/10.11606/T.45.2018.tde-05072018-164225
Document
Author
Full name
Eliardo Guimarães da Costa
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2017
Supervisor
Committee
Singer, Julio da Motta (President)
Achcar, Jorge Alberto
Andrade Filho, Mário de Castro
Paulino, Carlos Daniel Mimoso
Stern, Rafael Bassi
Title in Portuguese
Tamanho amostral para estimar a concentração de organismos em água de lastro: uma abordagem bayesiana
Keywords in Portuguese
Critério da cobertura média
Critério do comprimento médio
Distribuição binomial negativa
Distribuição Poisson
Processo Dirichlet
Risco de Bayes
Abstract in Portuguese
Metodologias para obtenção do tamanho amostral para estimar a concentração de organismos em água de lastro e verificar normas internacionais são desenvolvidas sob uma abordagem bayesiana. Consideramos os critérios da cobertura média, do tamanho médio e da minimização do custo total sob os modelos Poisson com distribuição a priori gama e binomial negativo com distribuição a priori Pearson Tipo VI. Além disso, consideramos um processo Dirichlet como distribuição a priori no modelo Poisson com o propósito de obter maior flexibilidade e robustez. Para fins de aplicação, implementamos rotinas computacionais usando a linguagem R.
Title in English
Sample size for estimating the organism concentration in ballast water: a Bayesian approach
Keywords in English
Average coverage criterion
Average length criterion
Bayes risk
Dirichlet process
Negative binomial distribution
Poisson distribution
Abstract in English
Sample size methodologies for estimating the organism concentration in ballast water and for verifying international standards are developed under a Bayesian approach. We consider the criteria of average coverage, of average length and of total cost minimization under the Poisson model with a gamma prior distribution and the negative binomial model with a Pearson type VI prior distribution. Furthermore, we consider a Dirichlet process as a prior distribution in the Poisson model with the purpose to gain more flexibility and robustness. For practical applications, we implemented computational routines using the R language.
 
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Costa_PhD_IME_USP.pdf (823.49 Kbytes)
Publishing Date
2018-07-06
 
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