As a contribution to literature, my work is a study to investigate a new way of classifying populations under the exponential power assumption. This was with a view to formulating an exponential power discriminant function and investigating the limiting case in accordance with an existing model. The discriminant function of the exponential power distribution was formulated using the Bayes maximum likelihood theorem The scale, location and the shape parameters were obtained numerically with the aid of Newton method in Matlab and R packages was used to obtain the Linear Discriminant Analysis (LDA) and the Quadractic Discriminant Analysis (QDA) of the data when the covariance matrices are equal and unequal respectively. Likewise Lachenbruch holdout procedure was used to check the error rates,whether it was advisable using the exponential power model or the existing procedure. The study concluded that exponential power distribution was a good replacement for normal distribution because it generalises the normal distribution.
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Oladipupo O. Onaseso, M.Sc: Studied Statistics at Obafemi Awolowo University Ile Ife. Lecturer at Moshood Abiola Polytechnic Abeokuta, Ogun State.
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Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -As a contribution to literature, my work is a study to investigate a new way of classifying populations under the exponential power assumption. This was with a view to formulating an exponential power discriminant function and investigating the limiting case in accordance with an existing model. The discriminant function of the exponential power distribution was formulated using the Bayes maximum likelihood theorem The scale, location and the shape parameters were obtained numerically with the aid of Newton method in Matlab and R packages was used to obtain the Linear Discriminant Analysis (LDA) and the Quadractic Discriminant Analysis (QDA) of the data when the covariance matrices are equal and unequal respectively. Likewise Lachenbruch holdout procedure was used to check the error rates,whether it was advisable using the exponential power model or the existing procedure. The study concluded that exponential power distribution was a good replacement for normal distribution because it generalises the normal distribution. 88 pp. Englisch. Bestandsnummer des Verkäufers 9786202027854
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Onaseso OladipupoOladipupo O. Onaseso, M.Sc: Studied Statistics at Obafemi Awolowo University Ile Ife. Lecturer at Moshood Abiola Polytechnic Abeokuta, Ogun State.As a contribution to literature, my work is a study to investigate. Bestandsnummer des Verkäufers 385901952
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -As a contribution to literature, my work is a study to investigate a new way of classifying populations under the exponential power assumption. This was with a view to formulating an exponential power discriminant function and investigating the limiting case in accordance with an existing model. The discriminant function of the exponential power distribution was formulated using the Bayes maximum likelihood theorem The scale, location and the shape parameters were obtained numerically with the aid of Newton method in Matlab and R packages was used to obtain the Linear Discriminant Analysis (LDA) and the Quadractic Discriminant Analysis (QDA) of the data when the covariance matrices are equal and unequal respectively. Likewise Lachenbruch holdout procedure was used to check the error rates,whether it was advisable using the exponential power model or the existing procedure. The study concluded that exponential power distribution was a good replacement for normal distribution because it generalises the normal distribution.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 88 pp. Englisch. Bestandsnummer des Verkäufers 9786202027854
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - As a contribution to literature, my work is a study to investigate a new way of classifying populations under the exponential power assumption. This was with a view to formulating an exponential power discriminant function and investigating the limiting case in accordance with an existing model. The discriminant function of the exponential power distribution was formulated using the Bayes maximum likelihood theorem The scale, location and the shape parameters were obtained numerically with the aid of Newton method in Matlab and R packages was used to obtain the Linear Discriminant Analysis (LDA) and the Quadractic Discriminant Analysis (QDA) of the data when the covariance matrices are equal and unequal respectively. Likewise Lachenbruch holdout procedure was used to check the error rates,whether it was advisable using the exponential power model or the existing procedure. The study concluded that exponential power distribution was a good replacement for normal distribution because it generalises the normal distribution. Bestandsnummer des Verkäufers 9786202027854
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Taschenbuch. Zustand: Neu. Discriminant Analysis of Data Under Exponential Power Distribution | Oladipupo Onaseso (u. a.) | Taschenbuch | 88 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9786202027854 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 110148776
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