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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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Journal ArticleDOI
01 Apr 2010-Ecology
TL;DR: It is shown how numerical integration via the Gauss-Hermite quadrature (GHQ) can be efficiently used to approximate the capture-recapture model likelihood with individual random effects and has potential important applications in population biology.
Abstract: In conservation and evolutionary ecology, quantifying and accounting for individual heterogeneity in vital rates of open populations is of particular interest. Individual random effects have been used in capture–recapture models, adopting a Bayesian framework with Markov chain Monte Carlo (MCMC) to carry out estimation and inference. As an alternative, we show how numerical integration via the Gauss-Hermite quadrature (GHQ) can be efficiently used to approximate the capture–recapture model likelihood with individual random effects. We compare the performance of the two approaches (MCMC vs. GHQ) and finite mixture models using two examples, including data on European Dippers and Sociable Weavers. Besides relying on standard statistical tools, GHQ was found to be faster than MCMC simulations. Our approach is implemented in program E-SURGE. Overall, capture–recapture mixed models (CR2Ms), implemented either via a GHQ approximation or MCMC simulations, have potential important applications in population biology.

118 citations

01 Oct 2010
TL;DR: Simulation results show that the proposed MERF method provides substantial improvements over standard RF when the random effects are non-negligible.
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.

118 citations

01 Nov 2007
TL;DR: In this article, the authors make use of longitudinal data for Denmark, taken from the waves 1995-1999 of the European Community Household Panel, and estimate fixed effects ordered logit models using the estimation methods proposed by Ferrer-i-Carbonel and Frijters (2004) and Das and van Soest (1999).
Abstract: A growing literature seeks to explain differences in individuals' self-reported satisfaction with their jobs. The evidence so far has mainly been based on cross-sectional data and when panel data have been used, individual unobserved heterogeneity has been modelled as an ordered probit model with random effects. This article makes use of longitudinal data for Denmark, taken from the waves 1995-1999 of the European Community Household Panel, and estimates fixed effects ordered logit models using the estimation methods proposed by Ferrer-i-Carbonel and Frijters (2004) and Das and van Soest (1999). For comparison and testing purposes a random effects ordered probit is also estimated. Estimations are carried out separately on the samples of men and women for individuals' overall satisfaction with the jobs they hold. We find that using the fixed effects approach (that clearly rejects the random effects specification), considerably reduces the number of key explanatory variables. The impact of central economic factors is the same as in previous studies, though. Moreover, the determinants of job satisfaction differ considerably between the genders, in particular once individual fixed effects are allowed for.

117 citations

Journal ArticleDOI
TL;DR: There is genetic variation that can be utilised for increasing longevity by selection in Swedish Landrace sows using a proportional hazards model based on the Weibull distribution.

117 citations

Journal ArticleDOI
TL;DR: The median rate ratio is the median relative change in the rate of the occurrence of the event when comparing identical subjects from 2 randomly selected different clusters that are ordered by rate and the variance partition coefficient is described, which denotes the proportion of the variation in the outcome that is attributable to between‐cluster differences that can be computed with count outcomes.
Abstract: Multilevel data occur frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models. These models incorporate cluster-specific random effects that allow one to partition the total variation in the outcome into between-cluster variation and between-individual variation. The magnitude of the effect of clustering provides a measure of the general contextual effect. When outcomes are binary or time-to-event in nature, the general contextual effect can be quantified by measures of heterogeneity like the median odds ratio or the median hazard ratio, respectively, which can be calculated from a multilevel regression model. Outcomes that are integer counts denoting the number of times that an event occurred are common in epidemiological and medical research. The median (incidence) rate ratio in multilevel Poisson regression for counts that corresponds to the median odds ratio or median hazard ratio for binary or time-to-event outcomes respectively is relatively unknown and is rarely used. The median rate ratio is the median relative change in the rate of the occurrence of the event when comparing identical subjects from 2 randomly selected different clusters that are ordered by rate. We also describe how the variance partition coefficient, which denotes the proportion of the variation in the outcome that is attributable to between-cluster differences, can be computed with count outcomes. We illustrate the application and interpretation of these measures in a case study analyzing the rate of hospital readmission in patients discharged from hospital with a diagnosis of heart failure.

117 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
2023198
2022433
2021409
2020380
2019404