Conditional and Marginal Models: Another View

Lee, Youngjo; Nelder, J. A.

doi:10.1214/088342304000000305

Cited by 176 publications

(173 citation statements)

References 25 publications

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“…, z q }, where often X ⊇ Z, are assumed to shift the conditional expectation E (y|u) = Xβ + Zu only, without affecting the response distribution in any other respect. However, a particular marginal model can be derived from (3) (Lee and Nelder 2004). Suppose X = Z and COV ( ) = ψ 2 I N .…”

Section: Linear Mixed Modelsmentioning

confidence: 99%

Linear Quantile Mixed Models: ThelqmmPackage for Laplace Quantile Regression

Geraci¹

2014

J. Stat. Soft.

183

156

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Inference in quantile analysis has received considerable attention in the recent years. Linear quantile mixed models (Geraci and Bottai 2014) represent a flexible statistical tool to analyze data from sampling designs such as multilevel, spatial, panel or longitudinal, which induce some form of clustering. In this paper, I will show how to estimate conditional quantile functions with random effects using the R package lqmm. Modeling, estimation and inference are discussed in detail using a real data example. A thorough description of the optimization algorithms is also provided.

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Section: Linear Mixed Modelsmentioning

confidence: 99%

Linear Quantile Mixed Models: ThelqmmPackage for Laplace Quantile Regression

Geraci¹

2014

J. Stat. Soft.

183

156

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“…For logistic models with normal random effects, one can always use the formulae discussed in sections 5 and 6. 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).…”

Section: Discussionmentioning

confidence: 99%

“…In fact, every SS model has a corresponding PA model (Lee & Nelder, 2004). For SS logistic models (including random intercepts and random slopes), there is a rule for converting the SS parameters to have a PA interpretation (see section 5).…”

Section: *1713 Individuals Are Omitted From Estimation Due To Unbalamentioning

confidence: 99%

Subject specific and population average models for binary longitudinal data: a tutorial

Szmaragd

Clarke

Steele

2013

LLCS

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SummaryUsing data from the British Household Panel Survey, we illustrate how longitudinal repeated measures of binary outcomes are analysed using population average and subject specific logistic regression models. We show how the autocorrelation found in longitudinal data is accounted for by both approaches, and why, in contrast to linear models for continuous outcomes, the parameters of population average and subject specific models for binary outcomes are different. To illustrate these points, we fit different models to our data set using both approaches, and compare and contrast the results obtained. Finally, we use our example to provide some guidance on how to choose between the two approaches.

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“…Surprisingly, marginal models are quite rarely employed within the behavioural sciences, despite the fact that Generalized estimating equations, i.e. the marginal modelling approach (Lee & Nelder, 2004;Ziegler, 2011), can handle correlated non-normally distributed (and heteroscedastic) data, which are, in fact, very common in the behavioural sciences. GEE is relatively easy to use.…”

Section: Analysis Of Non-normal Correlated Datamentioning

confidence: 99%

Generalized estimating equations: A pragmatic and flexible approach to the marginal GLM modelling of correlated data in the behavioural sciences

2017

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Within behavioural research, non-normally distributed data with a complicated structure are common. For instance, data can represent repeated observations of quantities on the same individual. The regression analysis of such data is complicated both by the interdependency of the observations (response variables) and by their non-normal distribution. Over the last decade, such data have been more and more frequently analysed using generalized mixed-effect models. Some researchers invoke the heavy machinery of mixed-effect modelling to obtain the desired population-level (marginal) inference, which can be achieved by using simpler tools-namely by marginal models. This paper highlights marginal modelling (using generalized estimating

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Conditional and Marginal Models: Another View

Cited by 176 publications

References 25 publications

Linear Quantile Mixed Models: ThelqmmPackage for Laplace Quantile Regression

Linear Quantile Mixed Models: ThelqmmPackage for Laplace Quantile Regression

Subject specific and population average models for binary longitudinal data: a tutorial

Generalized estimating equations: A pragmatic and flexible approach to the marginal GLM modelling of correlated data in the behavioural sciences

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