GNR, MGR, and exact misspeclfication testing
证明米利肯-格雷比尔回归是一种高斯-牛顿回归，从而解释了为何在特定非线性模型中高斯-牛顿回归能给出精确的F检验。
The Gauss-Newton regression (GNR) is widely used to compute Lagrange multiplier statistics. A regression described by Milliken and Graybill yields an exact F test in a certain class of nonlinear models which are linear under the null. This paper shows that the Milliken-Graybill regression is a GNR. Hence one interpretation of Milliken-Graybill is that they identified a class of nonlinear models for which the GNR yields an exact test.