[Download] "Kernel Smoothing in Matlab: Theory And Practice of Kernel Smoothing" by Ivanka Horová, Jan Koláček & Jiří Zelinka * eBook PDF Kindle ePub Free
eBook details
- Title: Kernel Smoothing in Matlab: Theory And Practice of Kernel Smoothing
- Author : Ivanka Horová, Jan Koláček & Jiří Zelinka
- Release Date : January 14, 2012
- Genre: Mathematics,Books,Science & Nature,Professional & Technical,
- Pages : * pages
- Size : 3487 KB
Description
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.
Sample Chapter(s)
Chapter 1: Introduction (195 KB)
Contents:IntroductionUnivariate Kernel Density EstimationKernel Estimation of a Distribution FunctionKernel Estimation and Reliability AssessmentKernel Estimation of a Hazard FunctionKernel Estimation of a Regression FunctionMultivariate Kernel Density Estimation
Readership: Advanced graduate students, researchers in mathematics or statistics.