Random effects model

Known as: Random-effect model, Variance components, Random effects estimator 
In statistics, a random effects model, also called a variance components model, is a kind of hierarchical linear model. It assumes that the data… (More)
Wikipedia

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2013
Highly Cited
2013
............................................................................................................................................................. 2 Acknowledgements ............................................................................................................................................ 3 
  • table 1
  • table 2
  • table 3
Is this relevant?
Highly Cited
2010
Highly Cited
2010
There are two popular statistical models for meta-analysis, the fixed-effect model and the random-effects model. The fact that… (More)
  • figure 1
  • figure 3
  • figure 4
Is this relevant?
Highly Cited
2009
Highly Cited
2009
Social network data often involve transitivity, homophily on observed attributes, clustering, and heterogeneity of actor degrees… (More)
  • table 1
  • figure 1
  • figure 2
  • figure 3
  • figure 4
Is this relevant?
Highly Cited
2007
Highly Cited
2007
This paper provides a non-technical introduction to mixed-effects models for the analysis of repeated measurement data with… (More)
  • table 1
  • figure 1
  • figure 2
  • table 2
  • table 3
Is this relevant?
Highly Cited
2007
Highly Cited
2007
Psycholinguistic data are often analyzed with repeated-measures analyses of variance (ANOVA), but this paper argues that mixed… (More)
  • table 1
  • table 2
  • figure 1
  • table 3
  • table 4
Is this relevant?
Highly Cited
2004
Highly Cited
2004
The penalized quasi-likelihood (PQL) approach is the most common estimation procedure for the generalized linear mixed model… (More)
  • table 1
  • table 2
  • table 3
  • figure 1
  • figure 2
Is this relevant?
Highly Cited
2003
Highly Cited
2003
Increased body weight is a strong risk factor for hypertension. A meta-analysis of randomized controlled trials was performed to… (More)
  • figure 1
  • figure 2
  • table 1
  • figure 3
  • table 2
Is this relevant?
Highly Cited
2003
Highly Cited
2003
A random effects model is proposed for the analysis of binary dyadic data that represent a social network or directed graph… (More)
  • table 1
Is this relevant?
Highly Cited
2002
Highly Cited
2002
Received analyses based on stochastic frontier modeling with panel data have relied primarily on results from traditional linear… (More)
  • table 1
  • figure 3
  • table 2
  • figure 4
  • figure 6
Is this relevant?