Smearing Estimate: A Nonparametric Retransformation Method
提出涂抹估计，用于在变换尺度拟合线性回归后，非参数地估计原始尺度的期望响应，具有一致性和高效率，在医学支出预测中优于参数估计。
The smearing estimate is proposed as a nonparametric estimate of the expected response on the untransformed scale after fitting a linear regression model on a transformed scale. The estimate is consistent under mild regularity conditions, and usually attains high efficiency relative to parametric estimates. It can be viewed as a low-premium insurance policy against departures from parametric distributional assumptions. A real-world example of predicting medical expenditures shows that the smearing estimate can outperform parametric estimates even when the parametric assumption is nearly satisfied.