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		<title>How To Generate Income Together with Rucaparib - Історія редагувань</title>
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		<updated>2026-05-07T06:18:35Z</updated>
		<subtitle>Історія редагувань цієї сторінки в вікі</subtitle>
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		<id>http://istoriya.soippo.edu.ua/index.php?title=How_To_Generate_Income_Together_with_Rucaparib&amp;diff=122002&amp;oldid=prev</id>
		<title>Knot32gallon: Створена сторінка: Statistical method We applied a bivariate linear mixed model framework (more details can be found in Refs. [3,4]) in order to test for association between indiv...</title>
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				<updated>2016-12-16T09:19:09Z</updated>
		
		<summary type="html">&lt;p&gt;Створена сторінка: Statistical method We applied a bivariate linear mixed model framework (more details can be found in Refs. [3,4]) in order to test for association between indiv...&lt;/p&gt;
&lt;p&gt;&lt;b&gt;Нова сторінка&lt;/b&gt;&lt;/p&gt;&lt;div&gt;Statistical method We applied a bivariate linear mixed model framework (more details can be found in Refs. [3,4]) in order to test for association between individual common genetic variant with SBP and DBP jointly. For trait k(k = 1,2), suppose Yik is a (ni �� 1) vector of the trait values for ni times of measurements for the subject ii=1,2,��,N; then, the univariate mixed-effect model with p independent variables with qq��p of them having random effects, can be expressed as [3,4] Yik=Xik��k+Zik��ik+Wik+��ik (1) where Xik is a ni��p design matrix that results in the [https://en.wikipedia.org/wiki/ALPI ALPI] systematic variation in the kth trait with ��k as the corresponding (p �� 1) vector of fixed-effect; Zik is a (ni �� q) design matrix, usually a subset of Xik(q��p) that characterize the random variation in the trait with ��ik~N(0,Gk) as the corresponding q��1 vector of random effect; Wik~N(0,Rik) is a (ni �� 1) vector of the stochastic processes (within subject errors over repeated [http://www.selleckchem.com/products/otx015.html OTX015] times) with realization wik(t) at time t with variance Rikt=��wk2 and covariance Riks,t=cov(wiks,wik(t))=��wk2e��k(t-s) at times s and t, 0 ��s Yi1Yi2=Xi100Xi2?��1��2+Zi100Zi2?��i1��i2+��i1��i2 (2) That is, Yi=Xi��+Zi��i+Wi+��i, where, ��i~N0,G;Wi~N0,Ri;?i~N0,��i; G=G1G12G12G2;��i=��?Ini;��=��?1200��?22. Here, ? is the Kronecker product. The Wi is the bivariate stochastic processes that not only captures the correlation of measurements within the same subject at multiple times, but also the correlation between 2 traits at the same time for the subject, and has the variance matrix Ri(t)=C=��w12��w1w2��w1w2��w22 at time t and covariance matrix Ri(s,t)=CeB(t-s) [http://www.selleckchem.com/products/AG-014699.html Rucaparib] at times t and s, 0��s&lt;/div&gt;</summary>
		<author><name>Knot32gallon</name></author>	</entry>

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