| 書目名稱 | Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed M | | 編輯 | George J. Knafl | | 視頻video | http://file.papertrans.cn/636/635954/635954.mp4 | | 概述 | Formulates and demonstrates novel extensions of standard methods for modeling correlated outcomes.Addresses linear, Poisson, logistic, exponential, multinomial, ordinal, and discrete regression.Covers | | 圖書封面 |  | | 描述 | This book formulates methods for modeling continuous and categorical correlated outcomes that extend 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 provides alternate estimating equations for mean parameters as well as estimating equations for variance/dispersion 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 Hessianmatrices, for a multi-step algorithm for solving estimating equations, and model-based and robust empirical tests for assessing theory-based models..Standard GEE, partially modified GEE, fully modified | | 出版日期 | Book 2023 | | 關(guān)鍵詞 | Adaptive Modeling; Correlated Outcomes; Extended Linear Mixed Modeling; Likelihood Cross-Validation; Gen | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-41988-1 | | isbn_softcover | 978-3-031-41990-4 | | isbn_ebook | 978-3-031-41988-1 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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