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.

Modelling catch and effort data using generalised linear models, the tweedie distribution, random vessel effects and random stratum-by-year effects

Abstract: The current standard method for modelling catch and effort data for Patagonian toothfi sh (Dissostichus eleginoides) for CCAMLR areas is to model the haul-by-haul ratios of catch to effort as the response variable in a generalised linear model (GLM) with a square-root link function and a unit variance function. A time series of standardised CPUE estimates and their precision can be obtained from the ‘fi shing year’ parameter estimates together with ‘baseline’ parameter estimates, their variance–covariance matrix, and the inverse-link function. An alternative GLM with a more rigorous theoretical basis is introduced here. Catch is modelled as the response variable using a GLM with a power variance function, with the power parameter (λ) estimated using a profi le extended quasi-likelihood, and a log link function with log of effort as an offset. For 1 < λ < 2 this model is equivalent to assuming a compound Poisson-gamma distribution (i.e. Tweedie distribution) for catch that, unlike lognormal or gamma distributions, admits zero values. In addition, random vessel effects are introduced into the GLM, as specifi ed by a generalised linear mixed model (GLMM), in order to provide more effi cient estimates of the standardised CPUE time series and more realistic estimates of their precision. Extra effi ciency is gained by recovery of inter-vessel information as a result of the imbalance in the number of hauls in the year-by-vessel cross-classifi cation. Further, the inclusion of an area stratum by fi year interaction as an additional random effect in the GLMM is investigated. Fitting the stratum-by-year interaction as a fi xed effect is problematic since it requires weighting of the individual stratum estimates by the areal extent of the stratum in order to obtain overall yearly standardised catch-per-unit-effort (CPUE) estimates. Without stratifi ed random sampling, the determination of stratum areas that will give unbiased standardised CPUE estimates may be diffi cult. Fitting the stratum-by-year interaction as a random effect avoids this diffi culty, and diagnostic methods to evaluate the validity of considering this interaction as random are described.

Copula-Based Random Effects Models for Clustered Data

Abstract: In a binary choice panel data framework, probabilities of the outcomes of several individuals depend on the correlation of the unobserved heterogeneity. I propose a random effects estimator that mo...

A Comparative Study of Alternative Estimators for Variance Components Models Used in

Abstract: The article investigates by means of Monte Carlo experiments the performance of the ML Inethod, the MINQUE method and several other two-step Generalized Least Squares methods in estimating the slope coefficient in a variance components model. It concludes that in models with no lagged dependent variables there is nothing much to choose among these estimators.

Testing overall and moderator effects in random effects meta-regression.

Abstract: Random effects meta-regression is a technique to synthesize results of multiple studies. It allows for a test of an overall effect, as well as for tests of effects of study characteristics, that is, (discrete or continuous) moderator effects. We describe various procedures to test moderator effects: the z, t, likelihood ratio (LR), Bartlett-corrected LR (BcLR), and resampling tests. We compare the Type I error of these tests, and conclude that the common z test, and to a lesser extent the LR test, do not perform well since they may yield Type I error rates appreciably larger than the chosen alpha. The error rate of the resampling test is accurate, closely followed by the BcLR test. The error rate of the t test is less accurate but arguably tolerable. With respect to statistical power, the BcLR and t tests slightly outperform the resampling test. Therefore, our recommendation is to use either the resampling or the BcLR test. If these statistics are unavailable, then the t test should be used since it is certainly superior to the z test.