The Principal Discriminant Method of Prediction: Theory and Evaluation
Abstract
The Principal Discriminant Method (PDM) of prediction employs a novel combination of principal component analysis and statistical discriminant analysis. Discriminant analysis is based on the construction of discrete category subsets of predictor values in a multidimensional predictor space. A category subset contains those predictor values which give rise to a predictand (or observation) in that particular category. A new predictor value is then assigned to a particular category (i.e., a forecast is made) through the use of probability distribution functions which have been fitted to the category subsets. The PDM uses principal component analysis to define the multidimensional probability distribution functions associated with the category subsets. Because of its underlying discriminant nature the PDM is also applicable to problems in data classification. The PDM is applied to prediction problems using both artificial and actual data sets. When applied to artificial data the PDM shows forecast skills which are comparable to those of standard forecast techniques, such as linear regression and classical discriminant analysis. Keywords: Reprint.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 20, 1988
- Accession Number
- ADA204311
Entities
People
- Curtis D. Mobley
- Rudolph W. Preisendorfer
- Tim P. Barnett
Organizations
- University of Washington