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Biometrika 1987 74(4):695-702; doi:10.1093/biomet/74.4.695
© 1987 by Biometrika Trust
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Estimating functions and approximate conditional likelihood

KUNG-YEE LIANG

Department of Biostatistics, Johns Hopkins University Baltimore, Maryland 21205, U.S.A.

The approximate conditional likelihood method proposed by Cox & Reid (1987) is applied to the estimation of a scalar parameter {Theta}, in the presence of nuisance parameters. The estimating function of {Theta} based on the approximate conditional likelihood is shown to be preferable to that based on the profile likelihood. A sufficient condition for both approaches to be equivalent is given. The role of parameter orthogonality is emphasized. Several examples including bivariate normal means with known coefficient of variation are presented.

Key Words: Asymptotics • Conditional inference: Estimating function • Nuisance parameter • Parameter orthogonality


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