Results 111 to 120 of about 1,186,982 (145)
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2017
This chapter deals with the most relevant multi-dimensional random effects panel data models, where, unlike the case of fixed effects, the number of parameters to be estimated does not increase with the sample size. First, optimal (F)GLS estimators are presented for the textbook-style complete data case, paying special attention to asymptotics.
Balazsi, Laszlo +3 more
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This chapter deals with the most relevant multi-dimensional random effects panel data models, where, unlike the case of fixed effects, the number of parameters to be estimated does not increase with the sample size. First, optimal (F)GLS estimators are presented for the textbook-style complete data case, paying special attention to asymptotics.
Balazsi, Laszlo +3 more
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Random effects Cox models: A Poisson modelling approach [PDF]
Biometrika, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Renjun Ma +2 more
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2002
Abstract Chapter 8 has dealt with marginal models whose regression parameters have population average interpretations. In this chapter we consider random effects models in which the regression coeficients measure the more direct infiuence of explanatory variables on the responses for heterogeneous individuals.
Peter J Diggle +3 more
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Abstract Chapter 8 has dealt with marginal models whose regression parameters have population average interpretations. In this chapter we consider random effects models in which the regression coeficients measure the more direct infiuence of explanatory variables on the responses for heterogeneous individuals.
Peter J Diggle +3 more
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2005
Abstract In Chapter 5 we found overdispersion in the fabric fault data; the Poisson GLM did not fit or represent the data adequately. The failure of a GLM to fit may be due to several causes. The distribution of Ymay not be the specified exponential family member, or the regression model fitted may be mis-specified.
Murray Aitkin, Brain Francis, John Hinde
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Abstract In Chapter 5 we found overdispersion in the fabric fault data; the Poisson GLM did not fit or represent the data adequately. The failure of a GLM to fit may be due to several causes. The distribution of Ymay not be the specified exponential family member, or the regression model fitted may be mis-specified.
Murray Aitkin, Brain Francis, John Hinde
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Random-Effects Models for Longitudinal Data
Biometrics, 1982Models for the analysis of longitudinal data must recognize the relationship between serial observations on the same unit. Multivariate models with general covariance structure are often difficult to apply to highly unbalanced data, whereas two-stage random-effects models can be used easily.
Laird, Nan M., Ware, James H.
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2009
Abstract In Chapter 5 we found overdispersion in the fabric fault data; the Poisson GLM did not fit or represent the data adequately. The failure of a generalized linear model to fit may be due to several causes. The distribution of Y may not be the specified exponential family member, or the regression model fitted may be mis-specified.
Murray Aitkin +3 more
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Abstract In Chapter 5 we found overdispersion in the fabric fault data; the Poisson GLM did not fit or represent the data adequately. The failure of a generalized linear model to fit may be due to several causes. The distribution of Y may not be the specified exponential family member, or the regression model fitted may be mis-specified.
Murray Aitkin +3 more
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Fixed- and Random-Effects Models
2021Deciding whether to use a fixed-effect model or a random-effects model is a primary decision an analyst must make when combining the results from multiple studies through meta-analysis. Both modeling approaches estimate a single effect size of interest.
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Fixed-effect Versus Random-effects Models for Meta-analyses: Fixed-effect Models
European Urology Focus, 2023A fixed-effect model considers a common underlying effect size for all the studies included in a meta-analysis. In the face of appreciable between-study heterogeneity, a fixed-effect model is a valuable tool when precision is the priority.
Hadi Mostafaei +2 more
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Random effects models in clinical research
Int. Journal of Clinical Pharmacology and Therapeutics, 2008In clinical trials a fixed effects research model assumes that the patients selected for a specific treatment have the same true quantitative effect and that the differences observed are residual error. If, however, we have reasons to believe that certain patients respond differently from others, then the spread in the data is caused not only by the ...
Cleophas, T. J., Zwinderman, A. H.
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Proportional hazards model with random effects
Statistics in Medicine, 2000We propose a general proportional hazards model with random effects for handling clustered survival data. This generalizes the usual frailty model by allowing a multivariate random effect with arbitrary design matrix in the log relative risk, in a way similar to the modelling of random effects in linear, generalized linear and non-linear mixed models ...
F, Vaida, R, Xu
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