Kernel Smoothing in MATLAB: Theory and Practice of Kernel Smoothing
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. The book provides a brief comprehensive overview of statistical theory and moreover, the emphasis is given to implementation of presented methods in Matlab. All created programs are included into 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. Especially, methods for 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 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. The book is intended for newcomers to the field of smoothing techniques and would be also appropriate for a wide audience: students and researches from both the statistical science and interface disciplines.