Generalized Order Statistics Under Finite Mixture Models: Asymptotic Theory and Applications

A. M. ELsawah

ISBN 10: 3659286060 / ISBN 13: 9783659286063

Verlag: LAP LAMBERT Academic Publishing 2014-03-18, 2014

Beschreibung

Bestandsnummer des Verkäufers 6666-IUK-9783659286063

Bibliografische Details

• Titel: Generalized Order Statistics Under Finite Mixture Models: Asymptotic Theory and Applications
• Verlag: LAP LAMBERT Academic Publishing 2014-03-18
• Erscheinungsjahr: 2014
• Sprache: Englisch
• ISBN-10: 3659286060
• ISBN-13: 9783659286063
• Einband: Paperback
• Zustand: New

Über diesen Titel

Inhaltsangabe

Finite mixture models have provided a mathematical-based approach in statistical modeling in a wide variety of random phenomena. FMM have been applied in astronomy, biology, genetic, medicine, psychiatry, economics, engineering and marketing, among many other fields in the biological, physical and social science. Kamps suggested a new theoretical approach, which is called generalized order statistics (GOS). This new model includes ordinary order statistics, sequential order statistics, progressive order statistics and record value. The main purpose of this book is to investigate the asymptotic behavior of the ordinary order statistics, generalized order statistics and dual generalized order statistics based on a random sample drawn from a finite mixture population with k components under general normalization. I obtain a sufficient conditions for this weak convergence, as well as the limit forms. Sufficient conditions are given to guarantee the existence of the weak convergence to non-degenerate distribution when the components are normalized by different normalization constants (linear-nonlinear). Illustrative examples of the most practically important distributions are obtained.

Über die Autorin bzw. den Autor

Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt. Dr. ELsawah research interests: Order Statistics and It's Application, Design and Modeling for Computer Experiments and Statistical Machine Learning. For more information, please visit Dr. Ahmed ELsawah web page: http://www.staff.zu.edu.eg/a.elsawah/page.aspid=43