000			04789nam a2200361Ia 4500
005			20250918233800.0
008			140402s2012 si a bs 001 0 eng d
020			_a9789814405485 _cRM290.32
020			_a9814405485
039		9	_a201406101518 _brosli _c201405201446 _drosli _c201405141309 _dhamudah _y04-02-2014 _zhamudah
040			_aBTCTA _beng _cBTCTA _dCDX _dOCLCO _dYDXCP _dUOH _dIUL _dDLC _dZ@L _dOCLCF _dVRC _dUKM
090			_aQA278.H637 3
090			_aQA278 _b.H637 3
100	1		_aHorova, Ivana.
245	1	0	_aKernel smoothing in MATLAB : _btheory and practice of Kernel smoothing / _cIvanka Horov Jan Kol{u2869}cek, Ji鲢i Zelinka.
260			_aSingapore : _bWorld Scientific Pub. Co., _c2012.
300			_axiii, 227 p. : _bill. ; _c24 cm.
504			_aIncludes bibliographical references (p. 213-223) and index.
505	0		_a1. Introduction. 1.1. Kernels and their properties. 1.2. Use of MATLAB toolbox. 1.3. Complements -- 2. Univariate kernel density estimation. 2.1. Basic definition. 2.2. Statistical properties of the estimate. 2.3. Choosing the shape of the kernel. 2.4. Choosing the bandwidth. 2.5. Density derivative estimation. 2.6. Automatic procedure for simultaneous choice of the kernel, the bandwidth and the kernel order. 2.7. Boundary effects. 2.8. Simulations. 2.9. Application to real data. 2.10. Use of MATLAB toolbox. 2.11. Complements -- 3. Kernel estimation of a distribution function. 3.1. Basic definition. 3.2. Statistical properties of the estimate. 3.3. Choosing the bandwidth. 3.4. Boundary effects. 3.5. Application to data. 3.6. Simulations. 3.7. Application to real data. 3.8. Use of MATLAB toolbox. 3.9. Complements -- 4. Kernel estimation and reliability assessment. 4.1. Basic definition. 4.2. Estimation of ROC curves. 4.3. Summary indices based on the ROC curve. 4.4. Other indices of reliability assessment. 4.5. Application to real data. 4.6. Use of MATLAB toolbox -- 5. Kernel estimation of a hazard function. 5.1. Basic definition. 5.2. Statistical properties of the estimate. 5.3. Choosing the bandwidth. 5.4. Description of algorithm. 5.5. Application to real data. 5.6. Use of MATLAB toolbox. 5.7. Complements -- 6. Kernel estimation of a regression function. 6.1. Basic definition. 6.2. Statistical properties of the estimate. 6.3. Choosing the bandwidth. 6.4. Estimation of the derivative of the regression function. 6.5. Automatic procedure for simultaneous choice of the kernel, the bandwidth and the kernel order. 6.6. Boundary effects. 6.7. Simulations. 6.8. Application to real data. 6.9. Use of MATLAB toolbox. 6.10. Complements -- 7. Multivariate kernel density estimation. 7.1. Basic definition. 7.2. Statistical properties of the estimate. 7.3. Bandwidth matrix selection. 7.4. A special case for bandwidth selection. 7.5. Simulations. 7.6. Application to real data. 7.7. Use of MATLAB toolbox. 7.8. Complements.
520			_a'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'--BOOK JACKET.
650		0	_aSmoothing (Statistics)
650		0	_aKernel functions.
700	1		_aKolacek, Jan.
700	1		_aZelinka, Jiri.
907			_a.b15863712 _b2019-11-12 _c2019-11-12
942			_c01 _n0 _kQA278.H637 3
914			_avtls003555305
990			_ark4
991			_aFakulti Sains dan Teknologi Maklumat
998			_al _b2014-02-04 _cm _da _feng _gsi _y0 _z.b15863712
999			_c566201 _d566201