Generalized Semiparametric Regression with Covariates Measured with Error
Generalized Semiparametric Regression with Covariates Measured with Error
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DOI：
10.1007/978-3-7908-2413-1_8
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发表时间：
2010-01-01
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影响因子：
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通讯作者：
Crainiceanu, Ciprian M.
中科院分区：
文献类型：
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作者：
Kneib, Thomas;Brezger, Andreas;Crainiceanu, Ciprian M.
我们开发了广义半参数回归模型的指数族和风险回归的多个协变量的测量误差和功能的形式，其影响仍然不明。在我们的方法的主要组成部分是贝叶斯惩罚样条和马尔可夫链蒙特卡罗模拟技术。这些使得基于真实的未观察到的协变量值的插补的贝叶斯测量误差校正的模块化和数字有效的实现成为可能。我们研究了模拟和流行病学研究中提出的校正的性能，其中检测心力衰竭的持续时间与肾功能和收缩压有关。
We develop generalized semiparametric regression models for exponential family and hazard regression where multiple covariates are measured with error and the functional form of their effects remains unspecified. The main building blocks in our approach are Bayesian penalized splines and Markov chain Monte Carlo simulation techniques. These enable a modular and numerically efficient implementation of Bayesian measurement error correction based on the imputation of true, unobserved covariate values. We investigate the performance of the proposed correction in simulations and an epidemiological study where the duration time to detection of heart failure is related to kidney function and systolic blood pressure.