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. This book provides a concise and comprehensive overview of statistical theory and in addition, emphasis is giv...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore :
World Scientific,
2012.
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| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 online resource |
| Bibliographie: | Includes bibliographical references (pages 213-223) and index. |
| ISBN: | 9789814405492 9814405493 |
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| 260 | |a Singapore : |b World Scientific, |c 2012. | ||
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| 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. | ||
| 505 | 0 | |a 1. 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. | |
| 504 | |a Includes bibliographical references (pages 213-223) and index. | ||
| 546 | |a English. | ||
| 650 | 0 | |a Smoothing (Statistics) |0 http://id.loc.gov/authorities/subjects/sh85123709 | |
| 650 | 0 | |a Kernel functions. |0 http://id.loc.gov/authorities/subjects/sh85072061 | |
| 650 | 6 | |a Lissage (Statistique) | |
| 650 | 6 | |a Noyaux (Mathématiques) | |
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| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a Kernel functions |2 fast | |
| 650 | 7 | |a Smoothing (Statistics) |2 fast | |
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| 700 | 1 | |a Koláček, Jan. | |
| 700 | 1 | |a Zelinka, Jiří. | |
| 776 | 0 | 8 | |i Print version: |t Kernel smoothing in MATLAB. |d Singapore : World Scientific, 2012 |w (DLC) 2012554726 |
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| contents | 1. 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. |
| ctrlnum | (OCoLC)811820296 |
| dewey-full | 519.5 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5 |
| dewey-search | 519.5 |
| dewey-sort | 3519.5 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Electronic eBook |
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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.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">1. 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. 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| id | ZDB-4-EBA-ocn811820296 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:24:59Z |
| institution | BVB |
| isbn | 9789814405492 9814405493 |
| language | English |
| lccn | 2012554726 |
| oclc_num | 811820296 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource |
| psigel | ZDB-4-EBA |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | World Scientific, |
| record_format | marc |
| spelling | Kernel smoothing in MATLAB : theory and practice of Kernel smoothing / edited by Ivanka Horová, Jan Koláček, Jiří Zelinka. Singapore : World Scientific, 2012. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier 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. 1. 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. Includes bibliographical references (pages 213-223) and index. English. Smoothing (Statistics) http://id.loc.gov/authorities/subjects/sh85123709 Kernel functions. http://id.loc.gov/authorities/subjects/sh85072061 Lissage (Statistique) Noyaux (Mathématiques) MATHEMATICS Applied. bisacsh MATHEMATICS Probability & Statistics General. bisacsh Kernel functions fast Smoothing (Statistics) fast Horová, Ivana. https://id.oclc.org/worldcat/entity/E39PCjJyVWD7WChx39G77T8qHC http://id.loc.gov/authorities/names/no2005063992 Koláček, Jan. Zelinka, Jiří. Print version: Kernel smoothing in MATLAB. Singapore : World Scientific, 2012 (DLC) 2012554726 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=491509 Volltext |
| spellingShingle | Kernel smoothing in MATLAB : theory and practice of Kernel smoothing / 1. 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. Smoothing (Statistics) http://id.loc.gov/authorities/subjects/sh85123709 Kernel functions. http://id.loc.gov/authorities/subjects/sh85072061 Lissage (Statistique) Noyaux (Mathématiques) MATHEMATICS Applied. bisacsh MATHEMATICS Probability & Statistics General. bisacsh Kernel functions fast Smoothing (Statistics) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85123709 http://id.loc.gov/authorities/subjects/sh85072061 |
| title | Kernel smoothing in MATLAB : theory and practice of Kernel smoothing / |
| title_auth | Kernel smoothing in MATLAB : theory and practice of Kernel smoothing / |
| title_exact_search | Kernel smoothing in MATLAB : theory and practice of Kernel smoothing / |
| title_full | Kernel smoothing in MATLAB : theory and practice of Kernel smoothing / edited by Ivanka Horová, Jan Koláček, Jiří Zelinka. |
| title_fullStr | Kernel smoothing in MATLAB : theory and practice of Kernel smoothing / edited by Ivanka Horová, Jan Koláček, Jiří Zelinka. |
| title_full_unstemmed | Kernel smoothing in MATLAB : theory and practice of Kernel smoothing / edited by Ivanka Horová, Jan Koláček, Jiří Zelinka. |
| title_short | Kernel smoothing in MATLAB : |
| title_sort | kernel smoothing in matlab theory and practice of kernel smoothing |
| title_sub | theory and practice of Kernel smoothing / |
| topic | Smoothing (Statistics) http://id.loc.gov/authorities/subjects/sh85123709 Kernel functions. http://id.loc.gov/authorities/subjects/sh85072061 Lissage (Statistique) Noyaux (Mathématiques) MATHEMATICS Applied. bisacsh MATHEMATICS Probability & Statistics General. bisacsh Kernel functions fast Smoothing (Statistics) fast |
| topic_facet | Smoothing (Statistics) Kernel functions. Lissage (Statistique) Noyaux (Mathématiques) MATHEMATICS Applied. MATHEMATICS Probability & Statistics General. Kernel functions |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=491509 |
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