Longitudinal Data Analysis. Handbooks of Modern Statistical Methods

By Geert Molenberghs. 2009

92 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6ed523223179c989a2a8da7f7c466cab63f2df33

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Citations in the corpus

Penalized Wavelets: Embedding Wavelets into Semiparametric Regression (2011, methodology): “MR2766861 [25] Fitzmaurice, G., Davidian, M., Verbeke, G. and Molenberghs, G. (Eds.)”
A Family of Generalized Linear Models for Repeated Measures With Normal and Conjugate Random Effects (pp.188) (2010, background): “As underscored by Verbeke and Molenberghs (2009), full verification of the adequacy of a random-effects structure is not possible based on statistical considerations alone, because there is a many-to-one map from hierarchical models to the implied marginal model.”
Covariance Estimation: The GLM and Regularization Perspectives (2011, methodology): “problem, (iv) the graphical LASSO algorithm of Friedman, Hastie and Tibshirani (2008, 2010); see also Friedman et al. (2009, Section 17.3), and (v) iteratively reweighted penalized likelihood of Fan, Feng and Wu (2010) where nonconcave penalties, such as the smoothly clipped absolute deviation (SCAD”
Estimation of extended mixed models using latent classes and latent processes: the R package lcmm (2015, methodology): “The generalized linear mixed model extends the theory to binary, ordinal or Poisson longitudinal outcomes (Hedeker and Gibbons 2006; Fitzmaurice et al. 2009).”
The Altruistic Rich? Inequality and Other-Regarding Preferences for Redistribution (2017, methodology): “To overcome this limitation, we specify a hierarchical model for state-specifc effects, yielding shrinkage estimates for preferences (Jiang 2007; Rabe-Hesketh and Skrondal 2008).”