Predicting corporate failure of UK's listed companies: Comparing
multiple discriminant analysis and logistic regression.
Their study concluded that
multiple discriminant analysis and recursive partitioning using decision tree gave the highest prediction accuracy for failed firms.
To perform the
multiple discriminant analysis of values and dimensions of organizational culture, we tested the assumptions of multivariate normality (using the Kolmogorov-Smirnov, or KS, test) and absence of outliers and homogeneity of the variance and covariance matrices (using Box's M test).
Assessment of company insolvency risk, based on
multiple discriminant analysis. Available from Internet: <www.zie.
He used
multiple discriminant analysis to find a weighting (the coefficients) and combination of financial variables that would correctly classify the firms into their respective credit risk categories.
Bascially,
multiple discriminant analysis tests whether samples from pre-assigned categores (in this case, macrohabitats) can be distinguished based on a set of associated characteristics (here, species or local environmental characteristics).
The main compositional approaches to the development of product spaces include common factor analysis (FA), principal components analysis (PCA) and
multiple discriminant analysis (MDA)[1].
The three empirical models used in the study are recursive partitioning, logistic regression, and
multiple discriminant analysis. For discussions of
multiple discriminant analysis and logit, which have been used extensively in previous insolvency studies, see BarNiv and Hershbarger (1990).
Multiple discriminant analysis (MDA) provides a procedure for assigning firms to predetermined groupings on the basis of variables whose values may depend on the group to which the firm actually belongs.
Multiple discriminant analysis was used to provide a better understanding of the motives guiding the order and coordination of relationships.
A stepwise
multiple discriminant analysis was computed in order to determine value and importance of these variables for discriminating among the three employment status groups.
G., "Bias in
Multiple Discriminant Analysis," Journal of Marketing Research, August 1965, 250-258.
