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• W2022654508 abstract "Abstract Linear and nonlinear exponential family and quasi-likelihood regression models form a class of models with a structure that invites using one algorithmic framework to compute parameter estimates and regression diagnostics. This framework extends our work on nonlinear least squares; it includes iteratively reweighted least squares but also encompasses secant updates for part of the Hessian matrix of the likelihood or quasi-likelihood function along with tests for when to use this information. The framework also provides basic machinery for computing “leave one out”-style regression diagnostics. We describe the framework, discuss some implementation details, and present some numerical experience." @default.
• W2022654508 created "2016-06-24" @default.
• W2022654508 creator A5081284390 @default.
• W2022654508 date "1988-12-01" @default.
• W2022654508 modified "2023-09-24" @default.
• W2022654508 title "Maximum Likelihood and Quasi-Likelihood for Nonlinear Exponential Family Regression Models" @default.
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• W2022654508 doi "https://doi.org/10.1080/01621459.1988.10478690" @default.
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