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- W2238639918 abstract "We occasionally find that a small subset of the data exerts a disproportionate influence on the fitted regression model. We would like to locate these influential points and assess their impact on the model. However, the existence of influential data is complicated by the presence of collinearity (see, e.g. [15 E. Walker and J. Birch, Influence measures in ridge regression, Technometrics 30 (1989), pp. 221–227. doi: 10.1080/00401706.1988.10488370[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]]). In this article we develop a new influence statistic for one or a set of observations in linear regression dealing with collinearity. We show that this statistic has asymptotically normal distribution and is able to detect a subset of high ridge leverage outliers. Using this influence statistic we also show that when ridge regression is used to mitigate the effects of collinearity, the influence of some observations can be drastically modified. As an illustrative example, simulation studies and a real data set are analysed." @default.
- W2238639918 created "2016-06-24" @default.
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- W2238639918 date "2015-09-02" @default.
- W2238639918 modified "2023-10-18" @default.
- W2238639918 title "New influence diagnostics in ridge regression" @default.
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- W2238639918 doi "https://doi.org/10.1080/02664763.2015.1070804" @default.
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