Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/12475
Title: Desempenho da medida L na seleção de modelos normais
Authors: Vivanco, Mario Javier Ferrua
Menezes, Fortunato Silva de
Bueno Filho, Júlio Sílvio de Sousa
Vivanco, Mario Javier Ferrua
Menezes, Menezes
Bueno Filho, Júlio Sílvio de Sousa
Sáfadi, Thelma
Lima, Renato Ribeiro de
Keywords: Inferência preditiva
Comparação de modelos
Medida L.
Predictive inference
Model comparison
Model comparison
Issue Date: 16-Mar-2017
Publisher: Universidade Federal de Lavras
Citation: VEIGA, E. P. Desempenho da Medida L na seleção de modelos normais. 2017. 103 p. Tese (Doutorado em Estatística e Experimentação Agropecuária)-Universidade Federal de Lavras, Lavras, 2016.
Abstract: Statistical models attempt to explain phenomena, natural or experimental. It is common to formulate more than one model to the same phenomenon and thus it is necessary to choose that one the best describes it. There are many criteria in the literature for comparison of models such as the Akaike information criterion (AIC), corrected Akaike criterion (AIC), Bayesian information criterion (BIC), among others, that try to minimize the loss of information in the modeling process. These criteria have asymptotic results. The L-measure is a measure for comparison of models concerned with the prediction values arising from the same or similar experiments using concepts such as predictive density in its definition, and thus, by comparing what is predicted to what is observed to make choice between models. In this work were calculated the rate of true positives (TP), false positives (FP), false negatives (FN) and true negatives (TN) for L-measure, as well as sensitivity to different sample sizes, smaller than 60. When considered predictive distributions quite close to the true predictive distribution, the results of the rates of TP and TN were low as well as the results for sensitivity. In other configurations considered for the study, with different predictive distributions from true predictive distribution, the results of the rates of TP and TN were high as well as the results for sensitivity. In general, the L-measure presented best performance than the AIC criteria, AIC and BIC for samples smaller than 60.
URI: http://repositorio.ufla.br/jspui/handle/1/12475
Appears in Collections:Estatística e Experimentação Agropecuária - Doutorado (Teses)

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