Topic
Random effects model
About: Random effects model is a research topic. Over the lifetime, 8388 publications have been published within this topic receiving 438823 citations. The topic is also known as: random effects & random effect.
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TL;DR: It is found that estimated fixed effects are compatible for all approaches, but that appropriate standard errors for the NPML require adjusting the likelihood-based standard errors.
Abstract: We discuss the performance of non-parametric maximum likelihood (NPML) estimators for the distribution of a univariate random effect in the analysis of longitudinal data. For continuous data, we analyse generated and real data sets, and compare the NPML method to those that assume a Gaussian random effects distribution and to ordinary least squares. For binary outcomes we use generated data to study the moderate and large-sample performance of the NPML compared with a method based on a Gaussian random effect distribution in logistic regression. We find that estimated fixed effects are compatible for all approaches, but that appropriate standard errors for the NPML require adjusting the likelihood-based standard errors. We conclude that the non-parametric approach provides an attractive alternative to Gaussian-based methods, though additional evaluations are necessary before it can be recommended for general use.
128 citations
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TL;DR: In this paper, a review of linear panel data models with slope heterogeneity is presented, along with various types of random coefficients models and a common framework for dealing with them, and the fundamental issues of statistical inference of a random coefficients formulation using both sampling and Bayesian approaches.
Abstract: This paper provides a review of linear panel data models with slope heterogeneity, introduces various types of random coefficients models and suggest a common framework for dealing with them. It considers the fundamental issues of statistical inference of a random coefficients formulation using both the sampling and Bayesian approaches. The paper also provides a review of heterogeneous dynamic panels, testing for homogeneity under weak exogeneity, simultaneous equation random coefficient models, and the more recent developments in the area of cross-sectional dependence in panel data models.
127 citations
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01 Jan 2002
TL;DR: Comparison of Two Samples, Linear Regression Model, Single#x2013 Factor Experiments with Fixed and Random Effects, and Statistical Analysis of Incomplete Data.
Abstract: Comparison of Two Samples- The Linear Regression Model- Single#x2013 Factor Experiments with Fixed and Random Effects- More Restrictive Designs- Incomplete Block Designs- Multifactor Experiments- Models for Categorical Response Variables- Repeated Measures Model- Cross#x2013 Over Design- Statistical Analysis of Incomplete Data
127 citations
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TL;DR: This paper aims to provide a comprehensive overview of available methods for calculating point estimates, confidence intervals, and prediction intervals for the overall effect size under the random‐effects model, and indicates whether some methods are preferable than others by considering the results of comparative simulation and real‐life data studies.
Abstract: Meta-analyses are an important tool within systematic reviews to estimate the overall effect size and its confidence interval for an outcome of interest. If heterogeneity between the results of the relevant studies is anticipated, then a random-effects model is often preferred for analysis. In this model, a prediction interval for the true effect in a new study also provides additional useful information. However, the DerSimonian and Laird method-frequently used as the default method for meta-analyses with random effects-has been long challenged due to its unfavorable statistical properties. Several alternative methods have been proposed that may have better statistical properties in specific scenarios. In this paper, we aim to provide a comprehensive overview of available methods for calculating point estimates, confidence intervals, and prediction intervals for the overall effect size under the random-effects model. We indicate whether some methods are preferable than others by considering the results of comparative simulation and real-life data studies.
127 citations
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TL;DR: In this paper, an extension of the random forest (RF) method to the case of clustered data is presented, which is implemented using a standard RF algorithm within the framework of the expectation-maximization algorithm.
Abstract: This paper presents an extension of the random forest (RF) method to the case of clustered data. The proposed ‘mixed-effects random forest’ (MERF) is implemented using a standard RF algorithm within the framework of the expectation–maximization algorithm. Simulation results show that the proposed MERF method provides substantial improvements over standard RF when the random effects are non-negligible. The use of the method is illustrated to predict the first-week box office revenues of movies.
127 citations