Conditional and Marginal Models: Another View
By Youngjo Lee, John A. Nelder. 2004
247 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/9c71f930fe54a7296d0c04424a887e9f2a4f2aa3
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- J. Neuhaus
- Wagner Hugo Bonat
- Charles E. McCulloch
- P. Clarke
- Matteo Bottai
- J. Signer
- Marco Geraci
- Stefanie Muff
In Statistics and Probability
- Wagner Hugo Bonat
- J. Neuhaus
- Bent Jørgensen
- Francis L. Huang
- Charles E. McCulloch
- Søren Højsgaard
- P. Clarke
- U. Halekoh
Citations in the corpus
- Probabilistic index models (2012, unknown): “The analysis of pain scores with PIMs would result in probabilities that quantify how likely it is that the pain will decrease as a function of a set of covariates. Pain may also be measured on the visual analogue scale of Wallerstein (1984). For this the patient is presented with a horizontal line ”
- Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation (2018, background): “GEEs, however, provide marginal parameter estimates that tend to underestimate the true effect sizes experienced by individual animals (Lee and Nelder, 2004; Fieberg et al., 2009; Muff et al., 2016); thus, we do not generally recommend them for habitat-selection studies.”
- Misspecifying the Shape of a Random Effects Distribution: Why Getting It Wrong May Not Matter (2011, background): “…marginalized mixed effects models (Heagerty and Zeger, 2000), h-likelihood approaches that can be easily adapted to fit different distributions (Lee and Nelder, 2004), families of parametric distributions (Piepho and McCul- 1 loch, 2004), mixtures of normal distributions (Lesaffre and…”
- Linear quantile mixed models (2013, background): “As mentioned previously, random-effects models allow conditional inferences in addition to marginal inferences (Robinson 1991; Lee and Nelder 2004).”
- The R Package geepack for Generalized Estimating Equations (2005, background): “For a discussion on the relation between marginal and mixed effects models, see Heagerty and Zeger (2000) and Nelder and Lee (2004).”
- Subject specific and population average models for binary longitudinal data: a tutorial (2013, methodology): “Conversely, it is impossible to obtain estimates of the SS parameters from a PA model because there are many SS models that correspond to the same PA model (Lee and Nelder, 2004).”
- Robustness of linear mixed‐effects models to violations of distributional assumptions (2020, unknown): “Mixed models are flexible, in principle, in their use of various distributions (Lee & Nelder, 2004), but normal distributions are by far the most commonly used.”
- Multivariate covariance generalized linear models (2015, methodology): “ariables, whereas the correlation structure is marginal for the latent variables rather than for the response variables. An interesting discussion of random-eects and marginal models may be found in Lee and Nelder (2004). Additional methods for specifying models for multivariate response variables i”