random effects model

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random effects model

A statistical model that may be used in meta-analysis, in which both within-study sampling error (variance) and between-studies variation are included in assessing the uncertainty or confidence interval of the results of the meta-analysis.
References in periodicals archive ?
In this and other ANOVA estimation procedures, calculation of these coefficients is a key step to estimation of variance components and F-statistics.
Although the conceptual approach has been understood for a number of years, statistical concerns about adequacy of the level-1 intercept and slope estimates as well as the estimation of the variance components hindered the full development of these models (see Burstein, Kim & Delandshere, 1987).
This Note argues that the ripeness test for substantive due process claims should include a finality requirement that consists of initial application and variance components but not a reapplication component, and a futility exception that extends to the variance component.
However, to obtain estimates of (co) variance components and genetic parameters for categorical data, some approaches that consider the discrete data distribution should be used to ensure greater accuracy of the estimates.
Keywords: Makooei Sheep, Body Weight, Random Regression Model, Variance Components, Legendre Polynomial.
Since no study has been done yet to estimate genetic parameters of reproductive traits in Kordi ewes of Northern Khorasan, this study aimed to estimate variance components as well as genetic parameters of reproductive traits in Kordi ewes.
Accurate estimates of the variance components of economically important traits are important in animal production because the differences between real and estimated values cause increase in the random-effect prediction error.
Hence, the objective of the present study was to estimate, fitting animal models, the variance components and genetic parameters for birth, weaning and yearling weights of Indubrazil cattle reared under tropical conditions of Mexico.
Therefore, the objective of this study was to investigate the effect of THI on the genetic variance components of daily milk yield and probable re-ranking of bulls in South Korea by estimated breeding value (EBV) under heat stress conditions.
The G study results indicated that among the variance components of the four dimensions, AOE had the largest values, followed by UOE, ROOE, and then ROE (see Table 1).
In addition, genetic variance components of variation like D (additive), H (dominance), E (environmental) were also estimated described by Mather and Jinks (1982).
In the GT context, ANOVA is used to estimate the magnitude of the variance components associated with the types of error identified by the researcher.