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Seite 127
V1 = • and is called intracluster correlation coefficient . Σ is a positive definite matrix . Thus we may assume Y ~ Nn , p ( .V.E ) ( see Arnold , 1981 , p311 ) and we have the linear model Y = XB + E ( 2.1 ) where E ~ Ν Nn ...
V1 = • and is called intracluster correlation coefficient . Σ is a positive definite matrix . Thus we may assume Y ~ Nn , p ( .V.E ) ( see Arnold , 1981 , p311 ) and we have the linear model Y = XB + E ( 2.1 ) where E ~ Ν Nn ...
Seite 270
[ I ] [ ] contains the coefficients corresponding to the terms of previous time step iteration as described in ( 12 ) . { X } T T T T T T = { { A } , { Ea ) , Iar ( Ep ) , Ib . ( Ec ) , Ic , ( Er ) , ( Ie } } k where { X } represents ...
[ I ] [ ] contains the coefficients corresponding to the terms of previous time step iteration as described in ( 12 ) . { X } T T T T T T = { { A } , { Ea ) , Iar ( Ep ) , Ib . ( Ec ) , Ic , ( Er ) , ( Ie } } k where { X } represents ...
Seite 338
In what follows we let Y1 = h ( x ) -E ( h ( xq ) ) , 1 ≤ i ≤ n and make the following i assumption on the strong mixing coefficient a ( n ) ( 3.23 ) ∞ ກ Σ na n = 1 6/4 ( n ) < ∞ . Then we have the following main theorem .
In what follows we let Y1 = h ( x ) -E ( h ( xq ) ) , 1 ≤ i ≤ n and make the following i assumption on the strong mixing coefficient a ( n ) ( 3.23 ) ∞ ກ Σ na n = 1 6/4 ( n ) < ∞ . Then we have the following main theorem .
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