In article <[EMAIL PROTECTED]>,
Robert Ehrlich <[EMAIL PROTECTED]> wrote:
>Calculation of eigenvalues and eigenvalues requires no assumption.
>However evaluation of the results IMHO implicitly assumes at least a
>unimodal distribution and reasonably homogeneous variance for the same
>reasons as ANOVA or regression. So think of th consequencesof calculating
>means and variances of a strongly bimodal distribution where no sample
>ocurrs near the mean and all samples are tens of standard devatiations
>from the mean.
Unimodality is not a concern at all. Asymptotic
distributions of moments only involve moments, and factor
analysis is carried out on sample moments.
One cannot have all observations "tens of standard
deviations from the mean". The Chebyshev inequality limits
how large the tails can be.
There are problems if the covariance matrix varies from
observation to observation, even with the same sample
structure. See my previous posting on what can be done
with weak assumptions.
>> I have a question regarding factor analysis: Is normality an important
>> precondition for using factor analysis?
>> If no, are there any books that justify this.
--
This address is for information only. I do not claim that these views
are those of the Statistics Department or of Purdue University.
Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399
[EMAIL PROTECTED] Phone: (765)494-6054 FAX: (765)494-0558
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