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Full random effects models (FREM): A practical usage guide. [PDF]
CPT Pharmacometrics Syst PharmacolJonsson EN, Nyberg J.
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Enhancing Cardiovascular Risk Prediction: Development of an Advanced Xgboost Model with Hospital-Level Random Effects. [PDF]
Bioengineering (Basel)Dong T +8 more
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Effective Properties of Random Composites
SIAM Journal on Scientific Computing, 2004Summary: We propose a new concept of effective properties of composites with uncertain spatial arrangements of constitutive materials and within-material properties. Rather than replacing a heterogeneous property with a constant effective parameter, we seek to preserve the internal macrostructure of a composite.
TARTAKOVSKY D. M., GUADAGNINI, ALBERTO
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Effective Randomness of Unions and Intersections
Theory of Computing Systems, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Douglas Cenzer, Rebecca Weber
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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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1994
This chapter is concerned with random effects models for analyzing nonnormal data that are assumed to be clustered or correlated. The clustering may be due to repeated measurements over time, as in longitudinal studies, or to subsampling the primary sampling units, as in cross-sectional studies.
Ludwig Fahrmeir, Gerhard Tutz
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This chapter is concerned with random effects models for analyzing nonnormal data that are assumed to be clustered or correlated. The clustering may be due to repeated measurements over time, as in longitudinal studies, or to subsampling the primary sampling units, as in cross-sectional studies.
Ludwig Fahrmeir, Gerhard Tutz
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A note regarding ‘random effects’
Statistics in Medicine, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Fixed-effect Versus Random-effects Models for Meta-analyses: Random-effects Models
European Urology Focus, 2023Random-effects models can account for variability both within and between studies. This makes them suitable for meta-analyses in surgery, where there is often significant heterogeneity between studies or heterogeneity owing to intrinsic differences attributable to patient or population factors.
Alex L.E. Halme +2 more
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Fixed Effects and Random Effects
2008One of the major benefits from using panel data as compared to cross-section data on individuals is that it enables us to control for individual heterogeneity. Not controlling for these unobserved individual specific effects leads to bias in the resulting estimates.
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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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