Inferência via Bootstrap na Conjoint Analysis
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2017-12-14
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Universidade Federal de Viçosa
Resumo
A presente tese teve como objetivo introduzir o método de reamostragem com reposição ou Bootstrap na Conjoint Analysis. Apresenta-se no texto uma revisão conceitual (Revisão de Literatura) sobre a referida metodologia (Conjoint Analysis) e também sobre o método proposto (Bootstrap). Adicionalmente, no Capítulo I e II, define-se a parte teórica e metodológica da Conjoint Analysis e do método Bootstrap, ilustrando o funcionamento conjunto dessas abordagens via aplicação real, com dados da área de tecnologia de alimentos. Inferências adicionais que até então não eram fornecidas no contexto clássico ou frequentista podem agora ser obtidas via análise das distribuições empíricas dos estimadores das Importâncias Relativas (abordagem por notas) e das Probabilidades e Razão de Escolhas (abordagem por escolhas). De forma geral, os resultados demonstraram que o método Bootstrap forneceu estimativas pontuais mais precisas e tornou ambas as abordagens da Conjoint Analysis mais informativas, uma vez que medidas de erro padrão e, principalmente, intervalos de confiança puderam ser facilmente obtidos para certas quantidades de interesse, possibilitando a realização de testes ou comparações estatísticas sobre as mesmas.
The aim of this thesis was introduce the Booststrap resampling method in Conjoint Analysis. We present in the text a conceptual review (Literature Review) about this methodology (Conjoint Analysis) and also about the proposed method (Bootstrap). In addition, in Chapter I and II, the theoretical and methodological aspects of Conjoint Analysis and the Bootstrap method are defined, illustrating the joint operation of these approaches via real application, with data from the food technology area.. Additional inferences have not been provided in the classic or frequentist context can now be obtained by analyzing the empirical distributions of Relative Importance (ratings based approach) and Probability and Choice Ratio (choice based approach) estimators. Overall, the results demonstrated that the Bootstrap method provided more accurate point estimates and made both Conjoint Analysis approaches more informative, since standard error measures, and mainly confidence intervals, could be easily obtained for certain quantities of interest, making it possible to perform statistical tests or comparisons on them.
The aim of this thesis was introduce the Booststrap resampling method in Conjoint Analysis. We present in the text a conceptual review (Literature Review) about this methodology (Conjoint Analysis) and also about the proposed method (Bootstrap). In addition, in Chapter I and II, the theoretical and methodological aspects of Conjoint Analysis and the Bootstrap method are defined, illustrating the joint operation of these approaches via real application, with data from the food technology area.. Additional inferences have not been provided in the classic or frequentist context can now be obtained by analyzing the empirical distributions of Relative Importance (ratings based approach) and Probability and Choice Ratio (choice based approach) estimators. Overall, the results demonstrated that the Bootstrap method provided more accurate point estimates and made both Conjoint Analysis approaches more informative, since standard error measures, and mainly confidence intervals, could be easily obtained for certain quantities of interest, making it possible to perform statistical tests or comparisons on them.
Descrição
Palavras-chave
Estatística - Modelos matemáticos, Marketing, Método Bootstrap
Citação
BARBOSA, Eduardo Campana. Inferência via Bootstrap na Conjoint Analysis. 2017. 57f. Tese (Doutorado em Estatística Aplicada e Biometria) - Universidade Federal de Viçosa, Viçosa. 2017.