Laird-Ware Random Effects Model
Fits the Laird-Ware Linear Random Effects Model. This model assumes that for each subject y=x*b+z*g+e where x and z are known m x p and m x r matricies, b is p x 1 parameter vector and g is a r vector which has a multivariate normal distribution with mean zero, e is a vector of identitcal independent normal mean zero random variables. The purpose is to estimate b, the variance covariance matrix of g and the variance of e.
Cite As
David Schoenfeld (2024). Laird-Ware Random Effects Model (https://www.mathworks.com/matlabcentral/fileexchange/20276-laird-ware-random-effects-model), MATLAB Central File Exchange. Retrieved .
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- AI, Data Science, and Statistics > Statistics and Machine Learning Toolbox > Probability Distributions >
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