| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 231.08 KB | Adobe PDF |
Advisor(s)
Abstract(s)
A model has an orthogonal block structure if it has, as covariance matrix, a linear
combination of pairwise orthogonal projection matrices, that add up to the iden-
tity matrix. The range space of these matrices are associated to hypotheses of an
orthogonal family.
In this paper we show how to obtain tests for these hypotheses when normality is
assumed and how to consider their relevance when normality is discarded. Besides
the notion of relevance, we formulate hypotheses in a general way that may be
applied to models with orthogonal block structure, whose factors may have xed
and/or random e ects. The results are applied to prime basis factorial models and
an example is presented.
Description
Keywords
Inference Mixed models Variance components Hypotheses relevancies Non-normality Prime basis factorials