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• W2080532349 abstract "In this article, we consider a general multivariate nonlinear regression setting in which the marginal mean and variance–covariance structure share a common set of regression parameters. Estimation is carried out via iteratively reweighted generalized least squares (IRGLS) that entails repeated application of Taylor series linearization and estimated generalized least squares (EGLS). Under normality, this IRGLS procedure is equivalent to Fisher's method of scoring and hence maximum likelihood estimation (MLE). However, estimates from this procedure are also shown to minimize a bias-corrected generalized least squares objective function that does not require the assumption of normality. Under fairly mild regularity conditions, the resulting estimates are consistent, asymptotically normal, and–under normality assumptions–asymptotically efficient. The estimates are compared against those obtained as solutions to the usual generalized estimating equations (GEE) using both simulation and numerical examples." @default.
• W2080532349 created "2016-06-24" @default.
• W2080532349 creator A5021703963 @default.
• W2080532349 creator A5051494127 @default.
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• W2080532349 date "2001-03-01" @default.
• W2080532349 modified "2023-09-26" @default.
• W2080532349 title "Generalized Least Squares, Taylor Series Linearization and Fisher's Scoring in Multivariate Nonlinear Regression" @default.
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• W2080532349 doi "https://doi.org/10.1198/016214501750332857" @default.
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