Acta mathematica scientia,Series B ›› 2000, Vol. 20 ›› Issue (1): 68-75.

• Articles • Previous Articles     Next Articles

ON CONFIDENCE REGIONS OF SEMIPARAMETRIC NONLINEAR REGRESSION MODELS (A GEOMETRIC APPROACH)

 ZHU Zhong-Yi, TANG Nian-Sheng, WEI Bo-Cheng   

  1. Department of Applied Mathematics, Southeast University, Nanjing 210096, China
  • Online:2000-01-07 Published:2000-01-07
  • Supported by:

    The project supported by NSFC and NSFJ

Abstract:

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.

Key words: Confidence regions, curvatures, nonlinear regression models, score statistic, semiparametric models

CLC Number: 

  • 62F25
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