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• W2345294455 abstract "ABSTRACTConstrained general linear models (CGLMs) have wide applications in practice. Similar to other data analysis, the identification of influential observations that may be potential outliers is an important step beyond in the CGLMs. We develop multiple case-deletion diagnostics for detecting influential observations in the CGLMs. The diagnostics are functions of basic building blocks: studentized residuals, error contrast matrix, and the inverse of the response variable covariance matrix. The basic building blocks are computed only once from the complete data analysis and provide information on the influence of the data on different aspects of the model fit. Computational formulas are given which make the procedures feasible. An illustrative example with a real data set is also reported." @default.
• W2345294455 created "2016-06-24" @default.
• W2345294455 creator A5003362945 @default.
• W2345294455 creator A5036664020 @default.
• W2345294455 date "2015-12-18" @default.
• W2345294455 modified "2023-09-24" @default.
• W2345294455 title "Influence diagnostics in constrained general linear models" @default.
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• W2345294455 doi "https://doi.org/10.1080/03610926.2014.942430" @default.
• W2345294455 hasPublicationYear "2015" @default.
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