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Image processing and jump regression analysis [[electronic resource] /] / Peihua Qiu
| Image processing and jump regression analysis [[electronic resource] /] / Peihua Qiu |
| Autore | Qiu Peihua <1965-> |
| Pubbl/distr/stampa | Hoboken, N.J., : John Wiley, c2005 |
| Descrizione fisica | 1 online resource (340 p.) |
| Disciplina | 006.3/7 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Image processing
Regression analysis |
| ISBN |
1-280-27685-1
9786610276851 0-470-35686-3 0-471-73315-6 0-471-73316-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Image Processing and Jump Regression Analysis; Contents; List of Figures; List of Tables; Preface; 1 Introduction; 1.1 Images and image representation; 1.1 A conventional coordinate system for expressing an image in industry.; 1.2 Regression curves and sugaces with jumps; 1.2 A log-transformed C-band, HH-polarization, synthetic aperture radar image of an area near Thetford forest, England.; 1.3 December sea-level pressures observed by a Bombay weather station in India during 1921-1992.; 1.3 Edge detection, image restoration, and jump regression analysis
1.4 Statistical process control and some other related topics1.5 Organization of the book; Problems; 2 Basic Statistical Concepts and Conventional Smoothing Techniques; 2.1 Introduction; 2.2 Some basic statistical concepts and terminologies; 2.2.1 Populations, samples, and distributions; 2.1 Probability density curve of the standard normal distribution.; 2.2.2 Point estimation of population parameters; 2.2.3 Confidence intervals and hypothesis testing; 2.2.4 Maximum likelihood estimation and least squares estimation; 2.3 Nadaraya- Watson and other kernel smoothing techniques 2.3.1 Univariate kernel estimators2.3.2 Some statistical properties of kernel estimators; 2.3.3 Multivariate kernel estimators; 2.4 Local polynomial kernel smoothing techniques; 2.4.1 Univariate local polynomial kernel estimators; 2.4.2 Some statistical properties; 2.2 The Nadaraya-Watson (NW) kernel estimator and the local linear kernel (LK) estimator.; 2.3 Behavior of the Nadaraya-Watson (NW) kernel estimator [plot (a)] and the local linear (LK) kernel estimator [plot (b)] of; 2.4.3 Multivariate local polynomial kernel estimators 2.4 Behavior of the Nadaraya- Watson (NW) kernel estimator [plot (a)] and the local linear kernel (LK) estimator [plot (b)] o2.4.4 Bandwidth selection; 2.5 Spline smoothing procedures; 2.5.1 Univariate smoothing spline estimation; 2.5.2 Selection of the smoothing parameter; 2.5.3 Multivariate smoothing spline estimation; 2.5.4 Regression spline estimation; 2.5 Four B-splines when ti, tj+1,tj+2, tj+3, and tj+4 are 0, 0.25, 0.5, 0.75, and 1.0.; 2.6 Wavelet transformation methods; 2.6.1 Function estimation based on Fourier transformation; 2.6.2 Univariate wavelet transformations 2.6 The Haar father wavelet, the Haar mother wavelet, the Haar wavelet function y1,0, and the Haar wavelet function y1,1.2.6.3 Bivariate wavelet transformations; Problems; 2.7 When f(x) and y(x) are the Haar father and mother wavelets, the two-dimensional wavelet functions F(x, y), Y(1)(x, y), Y(2)(x, y), and Y(3)(x, y) are displayed.; 3 Estimation of Jump Regression Curves; 3.1 Introduction; 3.2 Jump detection when the number of jumps is known; 3.2.1 Difference kernel estimation procedures 3.1 The true regression function f and the jump detection criterion MDKE dejined by expression (3.2) when c = 0,n = 100, and hn = 0.1. |
| Record Nr. | UNINA-9910143574403321 |
Qiu Peihua <1965->
|
||
| Hoboken, N.J., : John Wiley, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Image processing and jump regression analysis / / Peihua Qiu
| Image processing and jump regression analysis / / Peihua Qiu |
| Autore | Qiu Peihua <1965-> |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : John Wiley, c2005 |
| Descrizione fisica | 1 online resource (340 p.) |
| Disciplina | 006.3/7 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Image processing
Regression analysis |
| ISBN |
9786610276851
9781280276859 1280276851 9780470356869 0470356863 9780471733157 0471733156 9780471733164 0471733164 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Image Processing and Jump Regression Analysis; Contents; List of Figures; List of Tables; Preface; 1 Introduction; 1.1 Images and image representation; 1.1 A conventional coordinate system for expressing an image in industry.; 1.2 Regression curves and sugaces with jumps; 1.2 A log-transformed C-band, HH-polarization, synthetic aperture radar image of an area near Thetford forest, England.; 1.3 December sea-level pressures observed by a Bombay weather station in India during 1921-1992.; 1.3 Edge detection, image restoration, and jump regression analysis
1.4 Statistical process control and some other related topics1.5 Organization of the book; Problems; 2 Basic Statistical Concepts and Conventional Smoothing Techniques; 2.1 Introduction; 2.2 Some basic statistical concepts and terminologies; 2.2.1 Populations, samples, and distributions; 2.1 Probability density curve of the standard normal distribution.; 2.2.2 Point estimation of population parameters; 2.2.3 Confidence intervals and hypothesis testing; 2.2.4 Maximum likelihood estimation and least squares estimation; 2.3 Nadaraya- Watson and other kernel smoothing techniques 2.3.1 Univariate kernel estimators2.3.2 Some statistical properties of kernel estimators; 2.3.3 Multivariate kernel estimators; 2.4 Local polynomial kernel smoothing techniques; 2.4.1 Univariate local polynomial kernel estimators; 2.4.2 Some statistical properties; 2.2 The Nadaraya-Watson (NW) kernel estimator and the local linear kernel (LK) estimator.; 2.3 Behavior of the Nadaraya-Watson (NW) kernel estimator [plot (a)] and the local linear (LK) kernel estimator [plot (b)] of; 2.4.3 Multivariate local polynomial kernel estimators 2.4 Behavior of the Nadaraya- Watson (NW) kernel estimator [plot (a)] and the local linear kernel (LK) estimator [plot (b)] o2.4.4 Bandwidth selection; 2.5 Spline smoothing procedures; 2.5.1 Univariate smoothing spline estimation; 2.5.2 Selection of the smoothing parameter; 2.5.3 Multivariate smoothing spline estimation; 2.5.4 Regression spline estimation; 2.5 Four B-splines when ti, tj+1,tj+2, tj+3, and tj+4 are 0, 0.25, 0.5, 0.75, and 1.0.; 2.6 Wavelet transformation methods; 2.6.1 Function estimation based on Fourier transformation; 2.6.2 Univariate wavelet transformations 2.6 The Haar father wavelet, the Haar mother wavelet, the Haar wavelet function y1,0, and the Haar wavelet function y1,1.2.6.3 Bivariate wavelet transformations; Problems; 2.7 When f(x) and y(x) are the Haar father and mother wavelets, the two-dimensional wavelet functions F(x, y), Y(1)(x, y), Y(2)(x, y), and Y(3)(x, y) are displayed.; 3 Estimation of Jump Regression Curves; 3.1 Introduction; 3.2 Jump detection when the number of jumps is known; 3.2.1 Difference kernel estimation procedures 3.1 The true regression function f and the jump detection criterion MDKE dejined by expression (3.2) when c = 0,n = 100, and hn = 0.1. |
| Record Nr. | UNINA-9910823948103321 |
|
Qiu Peihua <1965->
|
||
| Hoboken, N.J., : John Wiley, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||