Semiparametric estimation in general repeated measures problems
Semiparametric estimation in general repeated measures problems
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DOI：
10.1111/j.1467-9868.2005.00533.x
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发表时间：
2006-01-01
影响因子：
5.8
通讯作者：
Carroll, RJ
中科院分区：
文献类型：
--
作者：
Lin, XH;Carroll, RJ
本文考虑了一类广泛的半参数问题的参数部分，一些协变量的影响和重复评价的非参数函数。在我们的方法的特殊情况下，包括纵向或集群数据的边缘模型，匹配的病例对照研究的条件logistic回归，多变量测量误差模型，广义线性混合模型与半参数组件，和许多其他。我们提出了这些问题的轮廓核和回拟合估计方法，推导出它们的渐近分布，并表明在似然问题的方法是半参数有效的。虽然通常不正确，但我们的方法分析和回拟合是渐进等价的。我们还考虑pseudolikestrine方法，其中一些滋扰参数估计从不同的算法。所提出的方法进行评估，通过使用模拟研究和应用到肯尼亚血红蛋白数据。
The paper considers a wide class of semiparametric problems with a parametric part for some covariate effects and repeated evaluations of a nonparametric function. Special cases in our approach include marginal models for longitudinal or clustered data, conditional logistic regression for matched case-control studies, multivariate measurement error models, generalized linear mixed models with a semiparametric component, and many others. We propose profile kernel and backfitting estimation methods for these problems, derive their asymptotic distributions and show that in likelihood problems the methods are semiparametric efficient. Although generally not true, it transpires that with our methods profiling and backfitting are asymptotically equivalent. We also consider pseudolikelihood methods where some nuisance parameters are estimated from a different algorithm. The methods proposed are evaluated by using simulation studies and applied to the Kenya haemoglobin data.