کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5129879 | 1489857 | 2017 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Uniformly most powerful unbiased test for conditional independence in Gaussian graphical model
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
آمار و احتمال
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Uniformly most powerful unbiased test for conditional independence in Gaussian graphical model Uniformly most powerful unbiased test for conditional independence in Gaussian graphical model](/preview/png/5129879.png)
چکیده انگلیسی
Model selection for Gaussian concentration graph is based on multiple testing of pairwise conditional independence. In practical applications partial correlation tests are widely used. However it is not known whether partial correlation test is uniformly most powerful for pairwise conditional independence testing. This question is answered in the paper. Uniformly most powerful unbiased test of Neyman structure is obtained. It turns out, that this test can be reduced to usual partial correlation test. It implies that partial correlation test is uniformly most powerful unbiased one.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Statistics & Probability Letters - Volume 122, March 2017, Pages 90-95
Journal: Statistics & Probability Letters - Volume 122, March 2017, Pages 90-95
نویسندگان
Petr Koldanov, Alexander Koldanov, Valeriy Kalyagin, Panos Pardalos,