A comparison of estimators of variance components in a two–way balanced crossed classification random effects model
DOI10.1080/02331889208802359zbMath0810.62067OpenAlexW2059831261MaRDI QIDQ4322928
Publication date: 3 April 1995
Published in: Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02331889208802359
independencenumerical integrationnormalityMonte Carlo simulationsmean squared errorsStein type estimatorsequivariant estimatorsBayesian estimatorsminimum variance unbiasedrestricted maximum likelihood estimatorsestimators of variance componentsmodel IIresidual variance componentstwo-way crossed classification random effects model
Point estimation (62F10) Bayesian inference (62F15) Analysis of variance and covariance (ANOVA) (62J10) Admissibility in statistical decision theory (62C15) Probabilistic methods, stochastic differential equations (65C99)
Cites Work
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- Nonnegative minimum biased invariant estimation in variance component models
- Bayes equivariant estimators in a crossed classification random effects model
- Inadmissibility of the usual estimator for the variance of a normal distribution with unknown mean
- A Note on the Generation of Random Normal Deviates
- Empirical bayes estimation of variance components in balanced random models
- The Problem of Negative Estimates of Variance Components
- SOME FORMAL BAYES ESTIMATORS OF VARIANCE COMPONENTS IN A CROSSED CLASSIFICATION RANDOM EFFECTS MODEL1
- A Comparison of Estimators of Variance Components in the Balanced Three-Stage Nested Random Effects Model Using Mean Squared Error Criterion
- Three modifications of the principle of the minque
- Components of Variance Estimation for the Split-Plot Design
- The Inverted Dirichlet Distribution with Applications
- Inadmissibility of the Usual Estimators of Scale parameters in Problems with Unknown Location and Scale Parameters
- Mean Square Efficiency of Estimators of Variance Components
- Formal Bayes Estimation with Application to a Random Effects Model
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