Model adequacy and influential observations
نویسندگان
چکیده
منابع مشابه
Detecting influential observations in Kernel PCA
Individual observations can be very influential when performing classical Principal Component Analysis in a Euclidean space. Robust PCA algorithms detect and neutralize such dominating data points. This paper studies robustness issues for PCA in a kernel induced feature space. The sensitivity of Kernel PCA is characterized by calculating the influence function. A robust Kernel PCA method is pro...
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Detecting outlying observations is an important step in any analysis, even when robust estimates are used. In particular, the robustified Mahalanobis distance is a natural measure of outlyingness if one focuses on ellipsoidal distributions. However, it is well known that the asymptotic chi-square approximation for the cutoff value of the Mahalanobis distance based on several robust estimates (l...
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ژورنال
عنوان ژورنال: Economics Letters
سال: 1992
ISSN: 0165-1765
DOI: 10.1016/0165-1765(92)90043-x