Sinopsis
In this monograph I present a method to derive a minimum rank covariance matrix for several continuous variables. The minimum rank problem appears in many areas of multivariate analysis as well as in many applications of multivariate analysis such as in biology, medicine, psychology, pharmacology, and machine learning. The method seems to be extremely powerful and enjoys many optimal properties. It is a non-linear distribution-free method that encompases under its umberla major topics such as Factor Analysis, Principal Componens Analysis, MDS, and Multiple Regression. The monograph is composed of two papers, the first of which sets the foundations and the theoratical basis for developing the underlying theory. It also presents several applications of the method. As for the second paper, it includes several examples of a completely different type of applications of the method. In these applications values of interactions are derived from only binary data such as low and high levels of interaction among pairs of objects. The key in all these applications is the low rank property of the covariance matrix which is the criterion for optimality of the method.
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Reseña del editor
In this monograph I present a method to derive a minimum rank covariance matrix for several continuous variables. The minimum rank problem appears in many areas of multivariate analysis as well as in many applications of multivariate analysis such as in biology, medicine, psychology, pharmacology, and machine learning. The method seems to be extremely powerful and enjoys many optimal properties. It is a non-linear distribution-free method that encompases under its umberla major topics such as Factor Analysis, Principal Componens Analysis, MDS, and Multiple Regression. The monograph is composed of two papers, the first of which sets the foundations and the theoratical basis for developing the underlying theory. It also presents several applications of the method. As for the second paper, it includes several examples of a completely different type of applications of the method. In these applications values of interactions are derived from only binary data such as low and high levels of interaction among pairs of objects. The key in all these applications is the low rank property of the covariance matrix which is the criterion for optimality of the method.
Biografía del autor
· Ph.D in Statistics from University of Wyoming, USA, 1986· Assistant Professor of Statistics in Kuwait University, Kuwait· Associate Professor of Statistics, American University of the Middle East· Statistical consultant UNDP – Kuwait· More than twenty international journal papers· More than 30 years academic and field experience
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Semi-Definite Programming as a Model for Statistical Data Analysis
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In this monograph I present a method to derive a minimum rank covariance matrix for several continuous variables. The minimum rank problem appears in many areas of multivariate analysis as well as in many applications of multivariate analysis such as in biology, medicine, psychology, pharmacology, and machine learning. The method seems to be extremely powerful and enjoys many optimal properties. It is a non-linear distribution-free method that encompases under its umberla major topics such as Factor Analysis, Principal Componens Analysis, MDS, and Multiple Regression. The monograph is composed of two papers, the first of which sets the foundations and the theoratical basis for developing the underlying theory. It also presents several applications of the method. As for the second paper, it includes several examples of a completely different type of applications of the method. In these applications values of interactions are derived from only binary data such as low and high levels of interaction among pairs of objects. The key in all these applications is the low rank property of the covariance matrix which is the criterion for optimality of the method. 60 pp. Englisch. Nº de ref. del artículo: 9783330853522
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Al-Ibrahim Abdul-Hameed- Ph.D in Statistics from University of Wyoming, USA, 1986- Assistant Professor of Statistics in Kuwait University, Kuwait- Associate Professor of Statistics, American University of the Middle East- Statistical. Nº de ref. del artículo: 151243390
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Semi-Definite Programming as a Model for Statistical Data Analysis
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ISBN 10: 3330853522
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Taschenbuch. Condición: Neu. Neuware -In this monograph I present a method to derive a minimum rank covariance matrix for several continuous variables. The minimum rank problem appears in many areas of multivariate analysis as well as in many applications of multivariate analysis such as in biology, medicine, psychology, pharmacology, and machine learning. The method seems to be extremely powerful and enjoys many optimal properties. It is a non-linear distribution-free method that encompases under its umberla major topics such as Factor Analysis, Principal Componens Analysis, MDS, and Multiple Regression. The monograph is composed of two papers, the first of which sets the foundations and the theoratical basis for developing the underlying theory. It also presents several applications of the method. As for the second paper, it includes several examples of a completely different type of applications of the method. In these applications values of interactions are derived from only binary data such as low and high levels of interaction among pairs of objects. The key in all these applications is the low rank property of the covariance matrix which is the criterion for optimality of the method.Books on Demand GmbH, Überseering 33, 22297 Hamburg 60 pp. Englisch. Nº de ref. del artículo: 9783330853522
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In this monograph I present a method to derive a minimum rank covariance matrix for several continuous variables. The minimum rank problem appears in many areas of multivariate analysis as well as in many applications of multivariate analysis such as in biology, medicine, psychology, pharmacology, and machine learning. The method seems to be extremely powerful and enjoys many optimal properties. It is a non-linear distribution-free method that encompases under its umberla major topics such as Factor Analysis, Principal Componens Analysis, MDS, and Multiple Regression. The monograph is composed of two papers, the first of which sets the foundations and the theoratical basis for developing the underlying theory. It also presents several applications of the method. As for the second paper, it includes several examples of a completely different type of applications of the method. In these applications values of interactions are derived from only binary data such as low and high levels of interaction among pairs of objects. The key in all these applications is the low rank property of the covariance matrix which is the criterion for optimality of the method. Nº de ref. del artículo: 9783330853522
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Taschenbuch. Condición: Neu. Semi-Definite Programming as a Model for Statistical Data Analysis | Abdul-Hameed Al-Ibrahim | Taschenbuch | 60 S. | Englisch | 2017 | Noor Publishing | EAN 9783330853522 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Nº de ref. del artículo: 108781570
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