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纵向数据比例似然比模型的矩型估计量AI 解读

Luo & Tsai, Biometrika99, 211–22, 2012, proposed a proportional likelihood ratio model and discussed a maximum likelihood method for its parameter estimation. In this paper, we use this model as the marginal distribution to analyse longitudinal data, where the maximum likelihood method is not directly applicable because the joint distribution is not fully specified. We propose a moment-type method that is an extension of the generalized estimating equation method. The…

比例似然比模型AI 解读

We propose a semiparametric proportional likelihood ratio model which is particularly suitable for modelling a nonlinear monotonic relationship between the outcome variable and a covariate. This model extends the generalized linear model by leaving the distribution unspecified, and has a strong connection with semiparametric models such as the selection bias model (Gilbert et al., 1999), the density ratio model (Qin, 1998; Fokianos & Kaimi, 2006), the single-index model…

右删失长度偏倚数据的非参数估计：伪偏似然方法AI 解读

To estimate the lifetime distribution of right-censored length-biased data, we propose a pseudo-partial likelihood approach that allows us to derive two nonparametric estimators. With its closed-form estimators and explicit limiting variances, this approach retains the simplicity of conditional analysis, and has only a small efficiency loss compared with the unconditional analysis. Under some regularity conditions, we show that the two estimators are uniformly consistent and…

有偏抽样数据下比例风险模型的伪偏似然AI 解读

We obtain a pseudo-partial likelihood for proportional hazards models with biased-sampling data by embedding the biased-sampling data into left-truncated data. The log pseudo-partial likelihood of the biased-sampling data is the expectation of the log partial likelihood of the left-truncated data conditioned on the observed data. In addition, asymptotic properties of the estimator that maximize the pseudo-partial likelihood are derived. Applications to length-biased data,…

缺失协变量下 Cox 回归模型的伪偏似然估计AI 解读

By embedding the missing covariate data into a left-truncated and right-censored survival model, we propose a new class of weighted estimating functions for the Cox regression model with missing covariates. The resulting estimators, called the pseudo-partial likelihood estimators, are shown to be consistent and asymptotically normal. A simulation study demonstrates that, compared with the popular inverse-probability weighted estimators, the new estimators perform better when…

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删失数据均值的 Buckley–James 型估计量AI 解读

It is shown that Buckley & James's (1979) estimator for the parameters in a linear regression model with censored data in the special case of the mean is identical to Susarla & Van Ryzin's (1980) estimator.