Modeling Correlated Outcomes Using Extensions Of Generalized Estimating Equations And Linear Mixed Modeling 2nd Edition (George J. Knafl)

Catergory: Nonfiction, Science & Nature, Mathematics, Statistics, Science

This book formulates methods for modeling continuous and categorical correlated outcomes extending the commonly used methods: generalized estimating equations (GEE) and linear mixed modeling. Partially modified GEE adds estimating equations for variance/dispersion parameters to the standard GEE estimating equations for the mean parameters. Fully modified GEE uses alternate estimating equations for the mean parameters. The new estimating equations in these two cases are generated by maximizing a "likelihood" function related to the multivariate normal density function. Partially modified GEE and fully modified GEE use the standard GEE approach to estimate correlation parameters based on the residuals. Extended linear mixed modeling (ELMM) uses the likelihood function to estimate not only mean and variance/dispersion parameters, but also correlation parameters. Formulations are provided for gradient vectors and Hessian matrices, for a multi-step algorithm for solving estimating equations, and model-based and robust empirical tests for assessing theory-based models. Directly specified correlation structures are considered as well as covariance structures based on random effect/coefficients.

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📌 Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling.epub (George J. Knafl) (2026) (36.9 MB)
📌 Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling.pdf (George J. Knafl) (2012) (12.08 MB)

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⭐️ Modeling Correlated Outcomes Using Extensions Of Generalized Estimating Equations And Linear Mixed Modeling 2nd Edition ✅ (49.98 MB)
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