Random-effects models for longitudinal data.

@article{Laird1982RandomeffectsMF,
  title={Random-effects models for longitudinal data.},
  author={Nan M. Laird and James Harold Ware},
  journal={Biometrics},
  year={1982},
  volume={38 4},
  pages={
          963-74
        }
}
Models for the analysis of longitudinal data must recognize the relationship between serial observations on the same unit. Multivariate models with general covariance structure are often difficult to apply to highly unbalanced data, whereas two-stage random-effects models can be used easily. In two-stage models, the probability distributions for the response vectors of different individuals belong to a single family, but some random-effects parameters vary across individuals, with a… 
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References

SHOWING 1-10 OF 26 REFERENCES
Analysis of growth and dose response curves.
TLDR
The method yields results identical to those obtained by weighting inversely by the sample covariance matrix, but has the additional feature of allowing flexibility in weighting by choosing subsets of covariates that have special properties.
Estimation in Covariance Components Models
Abstract Estimation techniques for linear covariance components models are developed and illustrated with special emphasis on explaining computational processes. The estimation of fixed and random
Missing Values in Multivariate Analysis
SUMMARY This paper presents computational results for some alternative methods of analysing multivariate data with missing values. We recommend an algorithm due to Orchard and Woodbury (1972), which
Linear regression with randomly dispersed parameters
SUMMARY Consider a collection of individual linear regression models, in which each individual parameter vector is independently drawn from a common multivariate normal distribution and is fixed over
The theory of least squares when the parameters are stochastic and its application to the analysis of growth curves.
TLDR
In the present paper, a class of problems where the dispersion matrix has a known structure is considered and the appropriate statistical methods are discussed.
A Bayesian approach to growth curves
SUMMARY Recent work on the analysis of growth curves has concentrated on the generalized growth model put forward by Potthoff & Roy (1964), the model being studied from a Bayesian viewpoint by
Simultaneous estimation of parameters in different linear models and applications to biometric problems.
Empirical Bayes procedure is employed in simultaneous estimation of vector parameters from a number of Gauss-Markoff linear models. It is shown that with respect to quadratic loss function, empirical
Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems
Abstract Recent developments promise to increase greatly the popularity of maximum likelihood (ml) as a technique for estimating variance components. Patterson and Thompson (1971) proposed a
Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
Vibratory power unit for vibrating conveyers and screens comprising an asynchronous polyphase motor, at least one pair of associated unbalanced masses disposed on the shaft of said motor, with the
Growth Curves
IN NATURE of January 19, 1946, there is a short communication by Prof. W. G. Burgers on “‘Stimulation Crystals’ and Twin-formation in Recrystallized Aluminium”. As indicated in ref. 1 to this
...
...