Durante mucho tiempo no había principios uniformes para la Atribución de nombres a los antibióticos https://antibioticos-wiki.es . Más a menudo se les llama por el nombre genérico o especie del producto, con menos frecuencia-de acuerdo con la estructura química. Algunos antibióticos se nombran de acuerdo con el lugar donde se asignó el producto.

Amt.ac.cn

Robust Inference in Semiparametric Models East China Normal University, Shanghai, China • Semiparametric Models for Longitudinal Data • Influence Diagnostics of Semiparametric Models 1. Case Deletion and Subject Deletion Analysis • Robust Estimation of Semiparametric Models {(xij, tij, yij), i = 1, . . . , m, j = 1, . . . , ni}. yij = XTijβ0 + g(tij) + eij, • β0 ∈ Rp , g is a smooth function , • eij are random errors, and are independent between subjects eij = ZTijbi + Ui(tij) + ij, n = • ni = 1: usually partialy linear model • In dependent data ni = 1 1. Speckman (1988),Hardle, Liang and Gao (2000):kernel 2. Heckman (1986), He and Shi (1996), Eubank (1999):spline • Longitudinal data and mixed models 1. Zeger and Diggle (1994): application to CD4 2. Moyeed and Diggle (1994): convergence rates 3. Zhang, Lin et al (1998), Lin and Zhang (1999), Ruppert, Wand and Carroll(2003): penalized likelihood 4. Lin and Carroll (2000,2001a,b,2002):GEE 1. Cook and Wesiberg (1982), Wei (1998), Banerjee and Frees (1997), Fung, Zhu, Wei and He (2002):Case Deletion 2. Cook (1986), Lu, Ko and Chang (1997), Lesaffre and Verbeke (1998), Zhu, He and He (2003):Local Influence Influence Diagnostics of Semiparametric Model L(β, g) = l(β, g) − g (t)dt = l(β, g) − gT Kg l(β, g) = − log |V | − (Y − Xβ − N g)T V −1(Y − Xβ − N g), • Penalized likelihood estimation H = I − ΣV −1 + ΣV −1 ¯ g N + λK)−1N T Wg dcdT • Cook distance of parameter β c WxX (X T WxX )−1X T Wxdc • Partial influence for nonparametric part g DFITij = |dTc N(ˆg(ij) − ˆg)|/sij where s2ij is the cth diagonal element of N(0, Ir)C−1(0, Ir)T NT θ = (βT , gT )T , Ei = (0, . . . , In , 0, . . . , 0)T , • Testing statistics of outlying subject Ti = ˆeT Ei(In − ¯ CD[i](β) = RTi Hβ,iRi Hβ,i = ETi WxX(XT WxX)−1XT WxEi CD[i](g) = RTi WgNS−1NTi (NiS−1NTi )−1NiS−1NT WgRi • Perturbation penalized likelihood L(θ, ω) θω is the estimate of θ under perturbation F = ∂2L(ˆθω) Fw = DT (ˆe)V −1 ¯ e1, · · · , ˆem) Partial influence matrix for parametric components β Fw(β) = DT (ˆe)WxX(XT WxX)−1XT WxD(ˆe) Partial influence matrix for nonparametric components g Fw(g) = DT (ˆe)WgN(NT WgN + λK)−1NT WgD(ˆe) Fe = DT (ΣV −1ˆe)V −1 ¯ Fe(g) = DT (ΣV −1ˆe)WgNS−1NT WgD(ΣV −1ˆe) Fe(β) = DT (ΣV −1ˆe)WxX(XT WxX)−1XT WxD(ΣV −1ˆe) e)WgNS−1NT WgD(ˆe) e)WxX(XT WxX)−1XT WxD(ˆe) H1)11, · · · , 1Tm(V −1 Fr(β) = 1TWxX(XT W X)−1XW Fr(g) = 1T WgX(XT W X)−1XT W • Data source Zhang, Lin et al (1998) • 34 women in one menstrual cycle, y =log progesterone level, x = age, BMI, t = days within one cycle yij = β1AGEi + β2BMIi + g(tij) + bi + Ui(tij) + ij ρ(yij − xTijβ − g(tij)) = minimum • Choice of ρ depends on interest 2. median ρ(r) = |r| ρ(r) = (τ I(r > 0) + (1 − τ )I(r < 0))|r| • Choice of smoothing method for g: kernel; local polynomial; π(t) = (B1(t), . . . , BN (t))T Order of polynomials l + 1; Knots: 0 = s0 < s1 · · · < sk = 1 sup |g(t) − αT π(t)| = O(k−r) where r is an order of smoothness of g.
• Advantages: Local smoothing but global representation, Good ρ(yij − xTijβ − π(tij)T α) = minimum Let θ = (βT , αT )T and zTij = (xTij, π(tTij))T . The problem is reducedto a linear model problem.
ψ(yij − zTijθ)zij = 0 2. ψ is not everywhere differentiable: Hunter and Lange (2000) • View as a model selection problem ij θ. This is a useful information-type criterion for N within a reasonable range • If k = kn → ∞ and k/n → 0, the estimate of (βT , g) is • If kn ≈ n1/(2r+1), we have ij ) − g(tij ))2 = Op(n−2r/(2r+1)), √n(ˆβ − β) → N(0,A−1BA−1) • A and B can be estimated consistently yij = xijβ + cos(πtij) + wi(tij) + ij • tij random sample from U(0, 1), xij = 5t2ij + N(0, 0.5) • wi(t) stationary Gaussian process with γ(t) = 0.4 exp(−η|t|) for • ZD-estimator as in Moyeed and Diggle (1994): uses kernel • Estimates of βj(j = 1, 2): • Non-significance of AGE and BMI in both mean and median

Source: http://www.amt.ac.cn/academic/workshop/workshop7/paper3.pdf