Variance Functions and the Minimum Detectable Concentration in Assays
研究了在非线性异方差回归模型中，方差函数中幂参数θ的估计方法如何影响最小可检测浓度的估计效率，发现不同方法效率差异显著，并通过渐近理论和模拟验证。
Assay data are often fitted by a nonlinear heteroscedastic regression model with the standard deviation of the response typically taken to be proportional to a power θ of the mean. For many assays, how one estimates θ does not greatly affect estimates of the mean regression function. Assay analysis also involves estimation of auxiliary calibration constructs such as minimum detectable concentration. An asymptotic theory is developed to show that standard methods for estimating θ lead to estimators for minimum detectable concentration that can differ markedly in efficiency. Simulation results support the asymptotic theory.