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Biometrika 1990 77(2):321-331; doi:10.1093/biomet/77.2.321
© 1990 by Biometrika Trust
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Approximate inferences in multiresponse regression analysis

GUNSEOG KANG and DOUGLAS M. BATES

Department of Statistics, Soongsil University Seoul Korea
Department of Statistics, University of Wisconsin-Madison Madison, Wisconsin 53706, U.S.A.

We present an approximate inference procedure for the multiresponse regression model. This can be used to determine approximate confidence regions or intervals and to formulate diagnostic tools such as standardized residuals and influence measures. The overall approximation is based on approximations at two stages. At the first stage, the multiresponse regression model is replaced by a generalized least-squares model using the estimated variance-covariance matrix of the error terms. At the second stage, the nonlinear model functions are replaced by linear approximations.

Key Words: Determinant criterion • Generalized sum of squares • Linear approximation • Maximum likelihood


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