Effects of Mahalanobis Distance and Prior Probabilities : On the Performance of the Linear and Quadratic Discriminant Functions: A Monte Carlo Approach

Language: English

Published by LAP LAMBERT Academic Publishing, 2011

3846524166 / 9783846524169

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nach der Bestellung gedruckt Neuware - Printed after ordering - In Discriminant analysis, one requires a relatively large training sample to construct a discriminant function and evaluate its performance. This is often unattainable in practice and so numerous Monte Carlo studies have been undertaken in an attempt to shed light on the asymptotic properties of classification functions. This empirical study examines the asymptotic performance of normal-based Linear and Quadratic discriminant functions for observations from two multivariate normal populations with different prior probabilities and varying between group distances. The sensitivity of these functions to increase in sample size, prior probability and Mahalanobis distance is investigated. Four sample size ratios of group1 to group2 (n1:n2) considered are: (1:1), (1:2), (1:3), and (1:4). Sample sizes ranging from 25 to 3,000 were considered for values 1 to 7 of group centroid separator. Multivariate normal observations were generated for two P-variate populations with p=4, p=6, and p=8 (LDF(4), LDF(6) , LDF(8), QDF(4), QDF(6) and QDF(8)) models. The respective sample size-ratio combinations were repeated for each level of with 100 replications for each scenario.…

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Title
Effects of Mahalanobis Distance and Prior Probabilities : On the Performance of the Linear and Quadratic Discriminant Functions: A Monte Carlo Approach
Author
Atinuke Adebanji
Publisher
LAP LAMBERT Academic Publishing
Publication year
2011
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3846524166
ISBN 13
9783846524169
Item weight
233 grams
Dimensions
220x150x10 mm

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