Effects of Mahalanobis Distance and Prior Probabilities: On the Performance of the Linear and Quadratic Discriminant Functions: A Monte Carlo Approach - Softcover
Synopsis
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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About the Author
Atinuke Adebanji (PhD University of Ibadan, 2006) is a senior lecturer with the Department of Mathematics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana (2009). She was a lecturer of Statistics at the Univ of Agric, Abeokuta, Nigeria (2003-2009). Her postgraduate theses were both on Discriminant Analysis.
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Effects of Mahalanobis Distance and Prior Probabilities
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 144 pp. Englisch. Seller Inventory # 9783846524169
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Adebanji AtinukeAtinuke Adebanji (PhD University of Ibadan, 2006) is a senior lecturer with the Department of Mathematics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana (2009). She was a lecturer of Statistics at . Seller Inventory # 5496573
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Effects of Mahalanobis Distance and Prior Probabilities
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 144 pp. Englisch. Seller Inventory # 9783846524169
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Effects of Mahalanobis Distance and Prior Probabilities : On the Performance of the Linear and Quadratic Discriminant Functions: A Monte Carlo Approach
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ISBN 10: 3846524166
ISBN 13: 9783846524169
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Taschenbuch. Condition: Neu. 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. Seller Inventory # 9783846524169
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Effects of Mahalanobis Distance and Prior Probabilities | On the Performance of the Linear and Quadratic Discriminant Functions: A Monte Carlo Approach
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Taschenbuch. Condition: Neu. Effects of Mahalanobis Distance and Prior Probabilities | On the Performance of the Linear and Quadratic Discriminant Functions: A Monte Carlo Approach | Atinuke Adebanji (u. a.) | Taschenbuch | 144 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783846524169 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Seller Inventory # 106735469
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Effects Of Mahalanobis Distance And Prior Probabilities: On The Performance Of The Linear And Quadratic Discriminant Functions: A Monte Carlo Approach
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