数学物理学报(英文版) ›› 2000, Vol. 20 ›› Issue (1): 68-75.
朱仲义, 唐年胜, 韦博成
ZHU Zhong-Yi, TANG Nian-Sheng, WEI Bo-Cheng
摘要:
A geometric framework is proposed for semiparametric nonlinear regression models based on the concept of least favorable curve, introduced by Severini and Wong (1992). The authors use this framework to drive three kinds of improved approximate confidence regions for the parameter and parameter subset in terms of curvatures. The results obtained by Hamilton et al. (1982), Hamilton (1986) and Wei (1994) are extended to semiparametric nonlinear regression models.
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