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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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TL;DR: This paper showed that in a very large number of models of interest to practioners, estimation of the fixed effects model is quite feasible even in panels with huge numbers of groups, and that the difficulty of estimating nonlinear models with possibly thousands of coefficients is not a nonissue.
Abstract: The application of nonlinear fixed effects models in econometrics has often been avoided for two reasons, one methodological, one practical. The methodological question centers on a incidental parametres problem that raises questions about the statistical properties of the estimator. The practical one relates to the difficulty of estimating nonlinear models with possibly thousands of coefficients. This note will demonstrate that the second is in fact, a nonissue, and that in a very large number models of interest to practioners, estimation of the fixed effects model is quite feasible even in panels with huge numbers of groups.

109 citations

Journal ArticleDOI
TL;DR: It is proved that, with the proxy matrix appropriately chosen, the proposed procedure can identify all true random effects with asymptotic probability one, where the dimension of random effects vector is allowed to increase exponentially with the sample size.
Abstract: This paper is concerned with the selection and estimation of fixed and random effects in linear mixed effects models. We propose a class of nonconcave penalized profile likelihood methods for selecting and estimating important fixed effects. To overcome the difficulty of unknown covariance matrix of random effects, we propose to use a proxy matrix in the penalized profile likelihood. We establish conditions on the choice of the proxy matrix and show that the proposed procedure enjoys the model selection consistency where the number of fixed effects is allowed to grow exponentially with the sample size. We further propose a group variable selection strategy to simultaneously select and estimate important random effects, where the unknown covariance matrix of random effects is replaced with a proxy matrix. We prove that, with the proxy matrix appropriately chosen, the proposed procedure can identify all true random effects with asymptotic probability one, where the dimension of random effects vector is allowed to increase exponentially with the sample size. Monte Carlo simulation studies are conducted to examine the finite-sample performance of the proposed procedures. We further illustrate the proposed procedures via a real data example.

109 citations

Journal ArticleDOI
TL;DR: In this article, the authors provide an overview of the rationale behind, and the implementation, and uses of the random coefficient approach to econometric modelling, and a simple random coefficient model is presented, and methods for estimating, testing, and validating such a model are described.
Abstract: . This paper provides an overview of the rationale behind, and the implementation, and uses of, the random coefficient approach to econometric modelling. A simple random coefficient model is presented, and methods for estimating, testing, and validating such a model are described. A more general model is then presented. The general model is shown to include several fixed-coefficient models as special cases and can be estimated incorporating a variety of judgements concerning simplification. Finally, the paper reviews recent applications of random coefficient estimation.

109 citations

Book
01 Aug 2014
TL;DR: The Cormorant data set, a model for estimating abundance in open populations, and Hierarchical modelling to allow for dependence of data sets are studied.
Abstract: Introduction History and motivation Marking Introduction to the Cormorant data set Modelling population dynamics Model fitting, averaging, and comparison Introduction Classical inference Bayesian inference Computing Estimating the size of closed populations Introduction The Schnabel census Analysis of Schnabel census data Model classes Accounting for unobserved heterogeneity Logistic-linear models Spuriously large estimates, penalized likelihood and elicited priors Bayesian modeling Medical and social applications Testing for closure-mixture estimators Spatial capture-recapture models Computing Survival modeling: single-site models Introduction Mark-recovery models Mark-recapture models Combining separate mark-recapture and recovery data sets Joint recapture-recovery models Computing Survival modeling: multi-site models Introduction Matrix representation Multi-site joint recapture-recovery models Multi-state models as a unified framework Extensions to multi-state models Model selection for multi-site models Multi-event models Computing Occupancy modelling Introduction The two-parameter occupancy model Extensions Moving from species to individual: abundance-induced heterogeneity Accounting for spatial information Computing Covariates and random effects Introduction External covariates Threshold models Individual covariates Random effects Measurement error Use of P-splines Senescence Variable selection Spatial covariates Computing Simultaneous estimation of survival and abundance Introduction Estimating abundance in open populations Batch marking Robust design Stopover models Computing Goodness-of-fit assessment Introduction Diagnostic goodness-of-fit tests Absolute goodness-of-fit tests Computing Parameter redundancy Introduction Using symbolic computation Parameter redundancy and identifiability Decomposing the derivative matrix of full rank models Extension The moderating effect of data Covariates Exhaustive summaries and model taxonomies Bayesian methods Computing State-space models Introduction Definitions Fitting linear Gaussian models Models which are not linear Gaussian Bayesian methods for state-space models Formulation of capture-re-encounter models Formulation of occupancy models Computing Integrated population modeling Introduction Normal approximations of component likelihoods Model selection Goodness of fit for integrated population modelling: calibrated simulation Previous applications Hierarchical modelling to allow for dependence of data sets Computing Appendix: Distributions reference Summary, Further reading, and Exercises appear at the end of each chapter.

109 citations

Journal ArticleDOI
TL;DR: The development of the expression of the Fisher information matrix in nonlinear mixed effects models for designs evaluation is extended and two methods using a Taylor expansion of the model around the expectation of the random effects or a simulated value are proposed and compared.
Abstract: We extend the development of the expression of the Fisher information matrix in nonlinear mixed effects models for designs evaluation. We consider the dependence of the marginal variance of the observations with the mean parameters and assume an heteroscedastic variance error model. Complex models with interoccasions variability and parameters quantifying the influence of covariates are introduced. Two methods using a Taylor expansion of the model around the expectation of the random effects or a simulated value, using then Monte Carlo integration, are proposed and compared. Relevance of the resulting standard errors is investigated in a simulation study with NONMEM.

109 citations


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