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Linear and generalized linear mixed effects models
2009In Chapter 8 we learned about the concept of hierarchical modeling, a data analysis approach that is appropriate when we have multiple measurements within each of several groups. In that chapter, variation in the data was represented with a between-group sampling model for group-specific means, in addition to a within-group sampling model to represent ...
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Empirical model selection in generalized linear mixed effects models
Computational Statistics, 2007This paper focuses on model selection in generalized linear mixed models using an information criterion approach. In these models in general, the response marginal distribution cannot be analytically derived. Thus, for parameter estimation, two approximations are revisited both leading to iterative model linearizations.
Christian Lavergne +2 more
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Diagnostics for repeated measurements in linear mixed effects models
Statistics in Medicine, 2012Most currently available methods for detecting discordant subjects and observations in linear mixed effects model fits adapt existing methods for single‐level regression data. The most common methods are generalizations of deletion‐based approaches, primarily Cook's distance.
Mun, Jungwon, Lindstrom, Mary J.
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Autoregressive Linear Mixed Effects Models
2018In the previous chapter, longitudinal data analysis using linear mixed effects models was discussed. This chapter discusses autoregressive linear mixed effects models in which the current response is regressed on the previous response, fixed effects, and random effects.
Ikuko Funatogawa, Takashi Funatogawa
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ℓ1-Penalized Linear Mixed-Effects Models for BCI
2011A recently proposed novel statistical model estimates population effects and individual variability between subgroups simultaneously, by extending Lasso methods. We apply this l1-penalized linear regression mixed-effects model to a large scale real world problem: by exploiting a large set of brain computer interface data we are able to obtain a subject-
Siamac Fazli +3 more
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Measuring explained variation in linear mixed effects models
Statistics in Medicine, 2003AbstractWe generalize the well‐knownR2measure for linear regression to linear mixed effects models. Our work was motivated by a cluster‐randomized study conducted by the Eastern Cooperative Oncology Group, to compare two different versions of informed consent document.
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Robust and nonparametric methods in linear models with mixed-effects
Metrika, 1995Summary: For linear models in a parametric mold, independence, homoscedasticity and normality of errors constitute the basic regularity conditions, and these have been relaxed to varying extents in alternative approaches based on robust and nonparametric methods.
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