<story><title>Mathematician uncovers way to shrink sampling errors in large-dimensional data</title><url>https://phys.org/news/2023-03-mathematician-uncovers-methods-sampling-errors.html</url></story><parent_chain></parent_chain><comment><author>godelski</author><text>paper: James–Stein for the leading eigenvector&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.pnas.org&amp;#x2F;doi&amp;#x2F;10.1073&amp;#x2F;pnas.2207046120&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.pnas.org&amp;#x2F;doi&amp;#x2F;10.1073&amp;#x2F;pnas.2207046120&lt;/a&gt;</text></comment>
<story><title>Mathematician uncovers way to shrink sampling errors in large-dimensional data</title><url>https://phys.org/news/2023-03-mathematician-uncovers-methods-sampling-errors.html</url></story><parent_chain></parent_chain><comment><author>ninepoints</author><text>Suggest changing the link to link the actual paper which suggests a method to reduce estimation error of the leading eigenvector</text></comment>