New PDF release: Circular binary segmentation for the analysis of array-based

By Olshen A.B., Venkatraman E.S.

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Then + (A + XBXT)-' = A-' - A-'XB(B + BXTA-'XB)-'BXTA-'. 2 Let where & is invertible. Then + xTA-'x). 6). Notice that specification ofH is flexible. For example, we may let H = XI,. This indicates that the components of are independent of each other. Moreover, when X + oc),we have H-' = X-'I, -+ 0. This indicates that the limit of the prior on P is non-informative. 15) as H + 0. That is, -' b= lim E(b1y). 17) where 2 = y - Xb - Zb and Proof see Appendix. b Notice that and b involve the unknown parameters D and R.

Can not be well fitted by lower degree polynomials. Hardle (1990), Fan and Gijbels (1 996), Green and Silverman (1 994), and Ramsay and Silverman (1 997,2002), among others, provide data examples where it is not possible to adequately fit the data using polynomials of any degree or any parametric models. In these cases, nonparametric modeling techniques are required. 1, can be denoted as ( t i ,Mi), i = 1,2 , . 1) where t i , i = 1,2, . ,n are known as design time points, and yi, i = 1,2,. ,n are the responses at the design time points.

21). 24). 4 below for easy reference. 3 but it is based on Theorem 2. I . 15) holds, and assume Ri,i = 1,2,. . 22). Then as H-l -+0, E(&21y) + N-’ E(Djy) -+ n-’ c;=] {;Tii+ 02 [nz- 0 2 t r ( P v i , ] } , -T CZ, { bibi + [D - D Z T P v , Z i D ] } w h e r e p v ; = vtyl- V F ’ X ~(C;=, X T V ; ~ X ~ )XTVII. -~ Proof: see Appendix. 1 PARAMETRIC MIXED-EFFECTS MODELS NONLINEAR MIXED-EFFECTS MODEL Model Specification The nonlinear mixed-effects (NLME) model is a natural generalization from the LME model.

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Circular binary segmentation for the analysis of array-based DNA copy number data by Olshen A.B., Venkatraman E.S.

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