作者：Huang, J
作者单位：University of Iowa
摘要：The partly linear additive Cox model is an extension of the (linear) Cox model and allows flexible modeling of covariate effects; semiparametrically. We study asymptotic properties of the maximum partial likelihood estimator of this model with right-censored data using; polynomial splines. We show that, with a range of choices of the smoothing parameter (the number of spline basis functions) required for estimation of the nonparametric components, the estimator of the finite-dimensional regres...
作者：Liang, H; Härdle, W; Carroll, RJ
作者单位：Chinese Academy of Sciences; Humboldt University of Berlin; Texas A&M University System; Texas A&M University College Station
摘要：We consider the partially linear model relating a response Y to predictors (X,T) with mean function X(inverted perpendicular)beta + g(T) when the X's are measured with additive error. The semiparametric likelihood estimate of Severini and Staniswalis leads to biased estimates of both the parameter beta and the function g((.))when measurement error is ignored. We derive a simple modification of their estimator which is a semiparametric version of the usual parametric correction for attenuation....
作者：Wood, GR
作者单位：Massey University
摘要：Given a mixture of binomial distributions, how do we estimate the unknown mixing distribution? We build on earlier work of Lindsay and further elucidate the geometry underlying this question, exploring the approximating role played by cyclic polytopes. Convergence of a resulting maximum likelihood fitting algorithm is proved and numerical examples given; problems over the lack of identifiability of the mixing distribution in part disappear.
