Measurement Error Regression with Unknown Link: Dimension Reduction and Data Visualization
研究了预测变量存在测量误差时的非线性回归问题，发现忽略测量误差直接使用普通最小二乘或切片逆回归仍能一致估计真实回归参数（至多相差一个比例常数），并推导了估计量的渐近分布。
A general nonlinear regression problem is considered with measurement error in the predictors. We assume that the response is related to an unknown linear combination of a multidimensional predictor through an unknown link function. Instead of observing the predictor, we instead observe a surrogate with the property that its expectation is linearly related to the true predictor with constant variance. We identify an important transformation of the surrogate variable. Using this transformed variable, we show that if one proceeds with the usual analysis ignoring measurement error, then both ordinary least squares and sliced inverse regression yield estimates which consistently estimate the true regression parameter, up to a constant of proportionality. We derive the asymptotic distribution of the estimates. A simulation study is conducted applying sliced inverse regression in this context.