کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1145833 | 1489680 | 2013 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Robust estimation of location and scatter by pruning the minimum spanning tree
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
آنالیز عددی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Robust estimation of location and scatter by pruning the minimum spanning tree Robust estimation of location and scatter by pruning the minimum spanning tree](/preview/png/1145833.png)
چکیده انگلیسی
One of the most essential topics in robust statistics is the robust estimation of location and covariance. Many popular robust (location and scatter) estimators such as Fast-MCD, MVE, and MZE require at least a convex distribution of the underlying data. In the case of non-convex data distributions these approaches may lead to a suboptimal result caused by the application of Mahalanobis distances with respect to location and covariance of a suitably chosen subsample of the data-implying a convex structure. The approach presented here fixes this drawback using Euclidean distances. The data set is treated as a complete network and the minimum spanning tree (MST) for this data set is calculated. Based on the MST a subset of relevant points (thought of as an “outlier-free” subsample of minimum size) is determined which can then be used for calculating data characteristics. It is shown, that the approach has a maximum breakdown point. Additionally, a simulation study provides insights in the approach's behaviour with respect to increasing dimension and size.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Multivariate Analysis - Volume 120, September 2013, Pages 173-184
Journal: Journal of Multivariate Analysis - Volume 120, September 2013, Pages 173-184
نویسندگان
Thomas Kirschstein, Steffen Liebscher, Claudia Becker,