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一般线性模型中响应变量的测量误差AI 解读

Problems where there is measurement error in the response variable in a general linear model are considered. With Y denoting the true value and U the observed/surrogate value, a rich class of measurement error models is considered in which (a) the mean of U given Y = y can be either linear or nonlinear in y, (b) the variance of U given Y = y can depend on y, and (c) the parameters in the model for U given Y can change across observations in the study. This could occur, for…

一般线性模型中响应变量的测量误差AI 解读

Problems where there is measurement error in the response variable in a general linear model are considered. With Y denoting the true value and U the observed/surrogate value, a rich class of measurement error models is considered in which (a) the mean of U given Y = y can be either linear or nonlinear in y, (b) the variance of U given Y = y can depend on y, and (c) the parameters in the model for U given Y can change across observations in the study. This could occur, for…

多元测量误差问题中获取真值的二阶段抽样AI 解读

Increasing attention is being given to measurement error models in which the dimension of the proxy or surrogate values is different from that of the missing true values. Even if they are of the same dimension, the error model may not be the simple additive one of observed = true + error, where the error has mean 0. The use of broader models relating true and observed values requires the use of external or internal data containing some true values to calibrate/validate the…