discriminant analysis

An alternative is the linear discriminant analysis, which does take this into account.

There were too few middle school non-respondents for a meaningful discriminant analysis.

Kernel discriminant analysis has been used in a variety of applications.

"Financial ratios, discriminant analysis and the prediction of corporate bankruptcy".

Discriminant analysis using these two latent factors would expect to perform very well in classification.

The use of discriminant analysis in marketing can be described by the following steps:

An eigenvalue in discriminant analysis is the characteristic root of each function.

However, when discriminant analysis' assumptions are met, it is more powerful than logistic regression.

Unlike logistic regression, discriminant analysis can be used with small sample sizes.

One popular example is Fisher's linear discriminant analysis.

At a more sophisticated level, computerised methods of a technique known as discriminant analysis can be invoked.

Discriminant analysis is used when groups are known a priori (unlike in cluster analysis).

Logistic regression does not have as many assumptions and restrictions as discriminant analysis.

Classification tree analysis has more recently been called "hierarchical optimal discriminant analysis".

With all this being considered, logistic regression is the common choice nowadays, since the assumptions of discriminant analysis are rarely met.

Using techniques such as discriminant analysis and cluster analysis, a model is being developed which represents the occupational structure for women.

Discriminant analysis indicated that expression profiles of these transcripts could be used to predict the trial arm to which patients belonged.

Validity: A discriminant analysis comparing "unambiguous exemplars of a single syndrome" was carried out.

Linear discriminant analysis is primarily used here to reduce the number of features to a more manageable number before classification.

Changing clinical spectrum, prevalence of hypertension, and discriminant analysis of laboratory tests.

A combined cluster and discriminant analysis approach revealed three clusters of dyslexics with different cognitive deficits.

It remains to be seen if they have applicability to the much more complex discriminant analysis problems routinely undertaken by classical taxonomists.

Altman's primary improvement was to apply a statistical method, discriminant analysis, which could take into account multiple variables simultaneously.

The goal of linear discriminant analysis is to give a large separation of the class means while also keeping the in-class variance small.

Classification tree analysis is a generalization of optimal discriminant analysis to non-orthogonal trees.