Estimating genetic correlations

DOI10.1111/j.1469-1809.1980.tb01560.xzbMath0421.92015OpenAlexW1998407585WikidataQ71153080 ScholiaQ71153080MaRDI QIDQ3854990

Cedric A. B. Smith

Publication date: 1980

Published in: Annals of Human Genetics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1469-1809.1980.tb01560.x

zbMATH Keywords

maximum likelihood estimatesintraclass correlationcross-correlationsiterative approximation proceduregenetic correlations

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Measures of association (correlation, canonical correlation, etc.) (62H20) Genetics and epigenetics (92D10) Analysis of variance and covariance (ANOVA) (62J10)

Related Items (8)

Resampling approaches for common intraclass correlation coefficients ⋮ The effect of kurtosis in the estimation of the parameters of the one-way random effects model from familial data ⋮ Comparison of estimators of interclass and intraclass correlations from familial data ⋮ Profile Likelihood Based Confidence Intervals for Common Intraclass Correlation Coefficient ⋮ Maximum likelihood estimation of sibling correlations in the analysis of family data ⋮ Cluster randomized trials: considerations for design and analysis ⋮ Estimation of intraclass correlation when family sizes are unequal ⋮ Bayesian estimation of the intraclass correlation coefficients in multivariate mixed linear model

Cites Work

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• Minimum variance quadratic unbiased estimation of variance components
• Restricted Maximum Likelihood (REML) Estimation of Variance Components in the Mixed Model
• Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems
• Maximum-likelihood estimation for the mixed analysis of variance model
• Estimation of variance and covariance components—MINQUE theory
• Estimation of Variance and Covariance Components in Linear Models
• Computing Maximum Likelihood Estimates for the Mixed A. O. V. Model Using the W Transformation
• The Combination of Estimates from Different Experiments
• The Derivation of Joint Distribution and Correlation between Relatives by the Use of Stochastic Matrices

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