Diagnostics for Assessing the Accuracy of Normal Approximations in Exponential Family Nonlinear Models
研究了评估指数族非线性模型中似然置信域与标准大样本置信域一致性的诊断方法，连接了轮廓方法、曲率度量和逻辑回归诊断，并用实例说明。
Abstract Diagnostics are investigated for assessing the agreement between likelihood and standard large-sample confidence regions for parameters from an exponential family nonlinear model (Cordeiro and Paula 1989). The development connects the contour methods proposed by Hodges (1985, 1987) with the curvature measures for normal, nonlinear regression proposed by Bates and Watts (1980) and Jennings's (1982, 1986) diagnostics for logistic regression. The proposed methodology is illustrated with several examples. Key Words: Exponential family nonlinear modelsLogistic regressionNonlinear regressionParameter-effects curvature