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Accuracy Evaluation of A Diagnostic Test by Detecting Outliers and Influential Observations 被引量:1
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作者 Hsien-Chueh Peter YANG Tsung-Hao CHEN +2 位作者 Cheng-Wu CHEN Chen-Yuan CHEN Chun-Te LIU 《China Ocean Engineering》 SCIE EI 2008年第3期421-429,共9页
Logit regression analysis is widely applied in scientific studies and laboratory experiments, where skewed observations on a data set are often encountered. A number of problems with this method, for example, oudiers ... Logit regression analysis is widely applied in scientific studies and laboratory experiments, where skewed observations on a data set are often encountered. A number of problems with this method, for example, oudiers and influential observations, can cause overdispersion when a model is fitted. In this study a systematic statistical approach, including the plotting of several indices is used to diagnose the lack-of-fit of a logistic regression model. The outliers and influential observations on data from laboratory experiments are then detected. Specifically we take account of the interaction of an internal sohtary wave (ISW) with an obstacle, i.e., an underwater ridge, and also analyze the effects of the ridge height, the lower layer water depth, and the potential energy on the amplitude-based transmission rate of the ISW. As concluded, the goodness-of-fit of the revised logit regression model is better than that of the model without this approach. 展开更多
关键词 diagnostic testing OUTLIERS influential observations internal solitary wave
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Influence Diagnostics in Partially Varying-Coefficient Models
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作者 Chun-xia Zhang Chang-lin Mei Jiang-she Zhang 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2007年第4期619-628,共10页
When a real-world data set is fitted to a specific type of models, it is often encountered that one or a set of observations have undue influence on the model fitting, which may lead to misleading conclusions. Therefo... When a real-world data set is fitted to a specific type of models, it is often encountered that one or a set of observations have undue influence on the model fitting, which may lead to misleading conclusions. Therefore, it is necessary for data analysts to identify these influential observations and assess their impact on various aspects of model fitting. In this paper, one type of modified Cook's distances is defined to gauge the influence of one or a set observations on the estimate of the constant coefficient part in partially varying- coefficient models, and the Cook's distances are expressed as functions of the corresponding residuals and leverages. Meanwhile, a bootstrap procedure is suggested to derive the reference values for the proposed Cook's distances. Some simulations are conducted, and a real-world data set is further analyzed to examine the performance of the proposed method. The experimental results are satisfactory. 展开更多
关键词 Partially varying-coefficient model influential observation Cook's distance cross-validation
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