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A letter commenting on the article "Tectonic control on development of a Frasnian-Famennian (Late Devonian) palaeokarst surface, Canning Basin reef complexes, northwestern Australia," by N. Chow and colleagues, which appeared in Vol. 51 is presented along with the authors' response.

This section presents a commentary to an article about the application of the population model to interpret randomized block designs. A confusion arises when a linear combination of parameters and random variable is used to describe the data. The assumptions of correlated interaction terms does not seem natural and it is just a consequence of the condition with is a carryover from the overparameterized fixed-effects model.

The objective of this article is to review existing methods for analyzing experimental design models with unbalanced data and to relate them to existing computer programs. The methods are distinguished by the hypotheses associated with the sums of squares which are generated. The choice of a method should be based on the appropriateness of the hypothesis rather than on computational convenience or the orthogonality of the quadratic forms. The sums of squares are described using the R ( ) notation as applied to the over-parameterized linear model, but the hypotheses are stated in terms of the full-rank cell means model. The zero-cell frequency situation is treated briefly. [ABSTRACT FROM AUTHOR]

In this paper a method is developed for estimating the parameters in the multivariate normal distribution in which the missing observations are not restricted to follow certain patterns as in most previous papers. The large sample properties of the estimators are discussed. Equivalence with maximum likelihood estimators has been established for a subclass of problems. The results of some simulation studies are provided to support the theoretical development. [ABSTRACT FROM AUTHOR]

A method is given here for fitting a quadratic subject to restrictions on the location of the extreme point which is conceptually somewhat different from that proposed by Cunia [1]. The solution is presented as a special case of the general quadratic programming problem, that is, minimizing a quadratic function subject to restrictions on the variables. [ABSTRACT FROM AUTHOR]