Recommended Readings
See the following texts for more information on variance components analysis (for complete bibliographic information, click on the reference) :
A discussion of choosing the appropriate type of sum of squares for the ANOVA method can be found in 1.
For a summary of the MINQUE method, see 2. For an in-depth discussion of the method, see 3. Further details of the MINQUE estimation procedure can be found in 4.
For technical details of computing the maximum likelihood estimates, see 5, 6, and 7.
For details about the restricted maximum likelihood estimates, see 8, 9, and 10.
1
Speed, F. M. 1979. Choice of sums of squares for estimation of components of
variance. In: Proceedings of the Statistical Computing Section, . , eds. Alexandria, Va.:
American Statistical Association.
2
Rao, C. R. 1973. Linear statistical inference and its applications, 2nd ed.
New York: John Wiley and Sons.
3
Rao, C. R., and J. Kleffe. 1988. Estimation of variance components and
applications. Amsterdam: North-Holland.
4
Giesbrecht, F. G. 1983. An efficient procedure for computing MINQUE of
variance components and generalized least squares estimates of fixed effects. Communications in
Statistics, Part A - Theory and Methods, 12:, 2169-2177.
5
Hemmerle, W. J., and H. O. Harley. 1973. Computing maximum likelihood
estimates for the mixed A.O.V. model using the W transformation. Technometrics, 15:,
819-831.
6
Jennrich, R. I., and P. F. Sampson. 1976. Newton-Raphson and related
algorithms for maximum likelihood variance component estimation. Technometrics, 18:,
11-17.
7
Searle, S. R., G. Casella, and C. E. McCulloch. 1992. Variance
Components. New York: John Wiley and Sons.
8
Patterson, H. D., and R. Thompson. 1971. Recovery of inter-block
information when block sizes are unequal. Biometrika, 58:, 545-554.
9
Corbeil, R. R., and S. R. Searle. 1976. Restricted maximum likelihood
(REML) estimation of variance components in the mixed model. Technometrics, 18:, 31-38.
10
Searle, S. R., G. Casella, and C. E. McCulloch. 1992. Variance
Components. New York: John Wiley and Sons.