Marginal Curvatures and Their Usefulness in the Analysis of Nonlinear Regression Models
研究了非线性回归中单个参数的区间估计与解轨迹曲率的关系，提出了判断曲率严重程度的标准和改善线性近似的方法，并给出了置信限的高阶修正公式。
Abstract Interval estimation for individual parameters in nonlinear regression is examined in relation to curvature of the solution locus. Criteria are developed to define severe curvature and distinguish cases in which linear methods may be used from those in which parameter transformations are necessary. A formula for improving the linear approximation in cases of relatively mild curvature is proposed. For example, suppose that interest is focused on one specific parameter, θ j from among θ = {θ1 θ2, …, θ p } in a nonlinear regression model. A crude approach to setting 95% confidence limits to θ j might proceed as follows: First calculate the standard error of the estimate as the square root of its asymptotic variance. Then call the confidence limits. In this article, higher-order correction terms Γ and β are proposed that enable us to develop a power series expansion for the confidence limits, as and respectively. Here denotes some estimate of σ2, the variance of a single observation. Key Words: Profile likelihood approximationsInterval estimation