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Smooth Random Effects Distribution in a Linear Mixed Model
Biometrics, 2004Summary A linear mixed model with a smooth random effects density is proposed. A similar approach to P‐spline smoothing of Eilers and Marx (1996, Statistical Science11, 89–121) is applied to yield a more flexible estimate of the random effects density.
Ghidey, Wendimagegn +2 more
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Generalized Multi-linear Mixed Effects Model
2016Recently, many applications tend to find common and distinctive features from a group of datasets, of which distributions and structures are generally various. However, most existing methods can just cope with specific problems with fixed distributions and structures.
Chao Li 0013 +4 more
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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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Mixed effect exponential linear model
Communications in Statistics - Theory and Methods, 1992A theory is developed for the estimation of linear functions of unknown parameters , hypothetical variables and a parameter θ in a mixed auss-Markoff linear model, , where each component is assumed to follow independent exponential distribution with parameter θ.
S.R. Patel, N.P. Patel
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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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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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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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Kernel estimator and predictor of partially linear mixed-effect errors-in-variables model
Journal of Statistical Computation and Simulation, 2021Seçil Yalaz
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