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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
TL;DR: In this article, a meta-analysis on influencing factors controlling the constant decay rate of coarse woody debris was set up, based on an intensive literature research a nonlinear mixed effects model was constructed.

89 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed a nonparametric method to quantify the covariation of components of multivariate longitudinal observations, which are viewed as realizations of a random process.
Abstract: Nonparametric methodology for longitudinal data analysis is becoming increasingly popular. The analysis of multivariate longitudinal data, where data on several time courses are recorded for each subject, has received considerably less attention, despite its importance for practical data analysis. In particular, there is a need for measures and estimates to capture dependency between the components of vector-valued longitudinal data. We propose and analyze a simple and effective nonparametric method to quantify the covariation of components of multivariate longitudinal observations, which are viewed as realizations of a random process. This includes the notion of a correlation between derivatives and time-shifted versions. The concept of dynamical correlation is based on a scalar product obtained from pairs of standardized smoothed curves. The proposed method can be used when observation times are irregular and not matching between subjects or between responses within a subject. For higher-dimensional dat...

89 citations

01 Jan 2004
TL;DR: In this article, the authors analyze a number of competing approaches to modeling efficiency in panel studies and compare their relative performances under a variety of misspecified settings using U. S. banking data.
Abstract: The paper analyzes a number of competing approaches to modeling efficiency in panel studies. The specifications considered include the fixed effects stochastic frontier, the random effects stochastic frontier, the Hausman-Taylor random effects stochastic frontier, and the random and fixed effects stochastic frontier with an AR(1) error. I have summarized the foundations and properties of estimators that have appeared elsewhere and have described the model assumptions under which each of the estimators have been developed. I discuss parametric and nonparametric treatments of time varying efficiency including the BatteseCoelli estimator and linear programming approaches to efficiency measurement. Monte Carlo simulation is used to compare the various estimators and to assess their relative performances under a variety of misspecified settings. A brief illustration of the estimators is conducted using U. S. banking data.

89 citations

Journal ArticleDOI
TL;DR: In this paper, an appropriate BIC expression that is consistent with the random effect structure of the mixed effects model is derived, which is used for variable selection in mixed effects models.
Abstract: The Bayesian Information Criterion (BIC) is widely used for variable selection in mixed effects models. However, its expression is unclear in typical situations of mixed effects models, where simple definition of the sample size is not meaningful. We derive an appropriate BIC expression that is consistent with the random effect structure of the mixed effects model. We illustrate the behavior of the proposed criterion through a simulation experiment and a case study and we recommend its use as an alternative to various existing BIC versions that are implemented in available software.

89 citations

Journal ArticleDOI
TL;DR: In this paper, the authors considered a Poisson model for the number of claims, while claim size is modelled using a Gamma distribution, allowing for dependencies between claim size and claim frequency.
Abstract: In this paper, models for claim frequency and average claim size in non-life insurance are considered. Both covariates and spatial random effects are included allowing the modelling of a spatial dependency pattern. We assume a Poisson model for the number of claims, while claim size is modelled using a Gamma distribution. However, in contrast to the usual compound Poisson model, we allow for dependencies between claim size and claim frequency. A fully Bayesian approach is followed, parameters are estimated using Markov Chain Monte Carlo (MCMC). The issue of model comparison is thoroughly addressed. Besides the deviance information criterion and the predictive model choice criterion, we suggest the use of proper scoring rules based on the posterior predictive distribution for comparing models. We give an application to a comprehensive data set from a German car insurance company. The inclusion of spatial effects significantly improves the models for both claim frequency and claim size, and also leads to mo...

89 citations


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