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Doctoral Thesis
DOI
10.11606/T.45.2013.tde-19082013-161233
Document
Author
Full name
Daniel Mendes Azerêdo
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2013
Supervisor
Committee
Stern, Julio Michael (President)
Lauretto, Marcelo de Souza
Nakano, Fábio
Pereira, Carlos Alberto de Braganca
Rifo, Laura Leticia Ramos
Title in Portuguese
Pesquisas sob amostragem informativa utilizando o FBST
Keywords in Portuguese
Amostragem Informativa
Amostragem PPT
Distribuição Amostral
FBST
Ignorabilidade amostral
Abstract in Portuguese
Pfeffermann, Krieger e Rinott (1998) apresentaram uma metodologia para modelar processos de amostragem que pode ser utilizada para avaliar se este processo de amostragem é informativo. Neste cenário, as probabilidades de seleção da amostra são aproximadas por uma função polinomial dependendo das variáveis resposta e concomitantes. Nesta abordagem, nossa principal proposta é investigar a aplicação do teste de significância FBST (Full Bayesian Significance Test), apresentado por Pereira e Stern (1999), como uma ferramenta para testar a ignorabilidade amostral, isto é, para avaliar uma relação de significância entre as probabilidades de seleção da amostra e a variável resposta. A performance desta modelagem estatística é testada com alguns experimentos computacionais.
Title in English
Surveys under informative sampling using the FBST
Keywords in English
Design Variables
FBST - Full Bayesian Significance Test
Informative Sampling
PPS Sampling
Sample Distribution
Sampling Ignorability
Abstract in English
Pfeffermann, Krieger and Rinott (1998) introduced a framework for modeling sampling processes that can be used to assess if a sampling process is informative. In this setting, sample selection probabilities are approximated by a polynomial function depending on outcome and auxiliary variables. Within this framework, our main purpose is to investigate the application of the Full Bayesian Significance Test (FBST), introduced by Pereira and Stern (1999), as a tool for testing sampling ignorability, that is, to detect a significant relation between the sample selection probabilities and the outcome variable. The performance of this statistical modelling framework is tested with some simulation experiments.
 
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Publishing Date
2013-08-22
 
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