Estimation of an errors-in-variables regression model when the variances of the measurement errors vary between the observations
Estimation of an errors-in-variables regression model when the variances of the measurement errors vary between the observations
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DOI：
10.1002/sim.1062
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发表时间：
2002-04-30
影响因子：
2
通讯作者：
Gasbarra, D
中科院分区：
文献类型：
--
作者：
Kulathinal, SB;Kuulasmaa, K;Gasbarra, D
在流行病学的聚集数据分析中，聚集观测值的方差是可用的。这种数据的分析导致测量误差的情况，其中测量误差的已知方差在观测之间变化。假设多元正态分布的“真”的观察和测量误差的正态分布，我们推导出一个简单的EM算法获得最大似然估计的多元正态分布的参数。这些结果也便于估计变量之间的回归参数以及观测值的“真”值。该方法被应用到重新估计最近的结果的世界卫生组织MONICA项目的心血管疾病及其危险因素，其中原始估计的回归系数没有调整的回归衰减所造成的测量误差。版权所有（C）2002约翰威利父子有限公司
It is common in the analysis of aggregate data in epidemiology that the variances of the aggregate observations are available. The analysis of such data leads to a measurement error situation, where the known variances of the measurement errors vary between the observations. Assuming multivariate normal distribution for the 'true' observations and normal distributions for the measurement errors, we derive a simple EM algorithm for obtaining maximum likelihood estimates of the parameters of the multivariate normal distributions. The results also facilitate the estimation of regression parameters between the variables as well as the 'true' values of the observations. The approach is applied to re-estimate recent results of the WHO MONICA Project on cardiovascular disease and its risk factors, where the original estimation of the regression coefficients did not adjust for the regression attenuation caused by the measurement errors. Copyright (C) 2002 John Wiley Sons, Ltd.