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CONSERVATIVE ESTIMATING FUNCTION IN THE NONLINEAR REGRESSION MODEL WITH AGGREGATED DATA 被引量:1

CONSERVATIVE ESTIMATING FUNCTION IN THE NONLINEAR REGRESSION MODEL WITH AGGREGATED DATA
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摘要 The purpose of this paper is to study the theory of conservative estimating functions in nonlinear regression model with aggregated data. In this model, a quasi-score function with aggregated data is defined. When this function happens to be conservative, it is projection of the true score function onto a class of estimation functions. By constructing, the potential function for the projected score with aggregated data is obtained, which have some properties of log-likelihood function. The purpose of this paper is to study the theory of conservative estimating functions in nonlinear regression model with aggregated data. In this model, a quasi-score function with aggregated data is defined. When this function happens to be conservative, it is projection of the true score function onto a class of estimation functions. By constructing, the potential function for the projected score with aggregated data is obtained, which have some properties of log-likelihood function.
作者 林路
出处 《Acta Mathematica Scientia》 SCIE CSCD 2000年第3期335-340,共6页 数学物理学报(B辑英文版)
基金 NNSF project 19771049 of China
关键词 nonlinear regression model with aggregated data quasi-score function conservative vector field potential function nonlinear regression model with aggregated data quasi-score function conservative vector field potential function
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参考文献5

  • 1Liu W Q,Pan J X.A New Estimate of the parameters in the linear model with aggregated data[].ChineseJournal of Engineering Mathematics.1996
  • 2Godambe V P,Heyde C D.Quai-likelihood and estimation[].International Statistical Review.1987
  • 3McLeish D L,Small C G.A projected likelihood function for semiparametric models[].Biometrika.1992
  • 4McLeish D L.Estimating for aggregate models: the aggregate markov chain[].Cannad J Statist.1984
  • 5Bind Li,McGullagh P.Potential functions and conservative estimating functions[].The Annals of Statistics.1994

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