Estimating Realized Random Effects in Mixed Models
نویسندگان
چکیده
A common analysis objective is estimation of a realized random effect. The parameter underlying such an effect is usually defined as an average response of a realized unit, such as a cluster mean, domain mean, small area mean, or subject effect. The effects are called random effects since their occurrence is the result of some (actual or assumed) random sampling process. In mixed models, random effects are commonly predicted using best linear unbiased predictors (BLUP). We develop an estimate of a realized random effect under a simple finite population two stage random permutation model using a prediction based approach. The estimate differs from the usual BLUP estimate, but is identical to the commonly used estimate of Scott and Smith (1969) that account for finite population sampling. We show that the estimate, and the MSE of the estimate will be identical with two variance structures, Scott and Smith's and the random permutation variance using a particular re-parameterization of the variance components. The model assumptions correspond to those typically used in survey sampling. The estimator is developed in a balanced setting, and extended to include possible response error.
منابع مشابه
Stochastic Restricted Two-Parameter Estimator in Linear Mixed Measurement Error Models
In this study, the stochastic restricted and unrestricted two-parameter estimators of fixed and random effects are investigated in the linear mixed measurement error models. For this purpose, the asymptotic properties and then the comparisons under the criterion of mean squared error matrix (MSEM) are derived. Furthermore, the proposed methods are used for estimating the biasing parameters. Fin...
متن کاملParameter Estimation in Spatial Generalized Linear Mixed Models with Skew Gaussian Random Effects using Laplace Approximation
Spatial generalized linear mixed models are used commonly for modelling non-Gaussian discrete spatial responses. We present an algorithm for parameter estimation of the models using Laplace approximation of likelihood function. In these models, the spatial correlation structure of data is carried out by random effects or latent variables. In most spatial analysis, it is assumed that rando...
متن کاملThe Application of Recursive Mixed Models for Estimating Genetic and Phenotypic Relationships between Calving Difficulty and Lactation Curve Traits in Iranian Holsteins: A Comparison with Standard Mixed Models
In the present study, records on 22872 first-parity Holsteins collected from 131 herds by the Animal Breeding and Improvement Center of Iran from 1995 to 2014 were considered to estimate genetic and phenotypic relationships between calving difficulty (CD) and the lactation curve traits, including initial milk yield (Ap), ascending (Bp) and descending (Cp) slope of the lactation curves, peak mil...
متن کاملMoment-based Method for Random Effects Selection in Linear Mixed Models.
The selection of random effects in linear mixed models is an important yet challenging problem in practice. We propose a robust and unified framework for automatically selecting random effects and estimating covariance components in linear mixed models. A moment-based loss function is first constructed for estimating the covariance matrix of random effects. Two types of shrinkage penalties, a h...
متن کامل