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Autor(en): 
  • Jan Kolácek
  • Ivanka Horová
  • Jirí Zelinka
  • Kernel Smoothing In Matlab: Theory And Practice Of Kernel Smoothing 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  September 2012  
    Genre:  Schulbücher 
     
    Kernel, Bandwidth, Density Estimate, Kernel Regression, Hazard Function / MATHEMATICS / Probability & Statistics / General / Probability & statistics / Probability and statistics
    ISBN:  9789814405485 
    EAN-Code: 
    9789814405485 
    Verlag:  World Scientific Publishing 
    Einband:  Gebunden  
    Sprache:  English  
    Seiten:  336 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Methods of kernel estimates represent one of the most effective nonparametric smoothing techniques. These methods are simple to understand and they possess very good statistical properties. This book provides a concise and comprehensive overview of statistical theory and in addition, emphasis is given to the implementation of presented methods in Matlab. All created programs are included in a special toolbox which is an integral part of the book. This toolbox contains many Matlab scripts useful for kernel smoothing of density, cumulative distribution function, regression function, hazard function, indices of quality and bivariate density. Specifically, methods for choosing a choice of the optimal bandwidth and a special procedure for simultaneous choice of the bandwidth, the kernel and its order are implemented. The toolbox is divided into six parts according to the chapters of the book.

    All scripts are included in a user interface and it is easy to manipulate with this interface. Each chapter of the book contains a detailed help for the related part of the toolbox too. This book is intended for newcomers to the field of smoothing techniques and would also be appropriate for a wide audience: advanced graduate, PhD students and researchers from both the statistical science and interface disciplines.

      



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