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Control theoretic splines [[electronic resource] ] : optimal control, statistics, and path planning / / Magnus Egerstedt and Clyde Martin
| Control theoretic splines [[electronic resource] ] : optimal control, statistics, and path planning / / Magnus Egerstedt and Clyde Martin |
| Autore | Egerstedt Magnus |
| Edizione | [Course Book] |
| Pubbl/distr/stampa | Princeton, : Princeton University Press, c2010 |
| Descrizione fisica | 1 online resource (227 p.) |
| Disciplina | 511/.42 |
| Altri autori (Persone) | MartinClyde |
| Collana | Princeton series in applied mathematics |
| Soggetto topico |
Interpolation
Smoothing (Numerical analysis) Smoothing (Statistics) Curve fitting Splines Spline theory |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-45796-9
1-282-93606-9 9786612936067 9786612457968 1-4008-3387-6 |
| Classificazione | SK 880 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Contents -- Preface -- Chapter One. Introduction -- Chapter Two. Control Systems and Minimum Norm Problems -- Chapter Three. Eight Fundamental Problems -- Chapter Four. Smoothing Splines and Generalizations -- Chapter Five. Approximations and Limiting Concepts -- Chapter Six. Smoothing Splines with Continuous Data -- Chapter Seven. Monotone Smoothing Splines -- Chapter Eight. Smoothing Splines as Integral Filters -- Chapter Nine. Optimal Transfer between Affine Varieties -- Chapter Ten. Path Planning and Telemetry -- Chapter Eleven. Node Selection -- Bibliography -- Index |
| Record Nr. | UNINA-9910456747503321 |
Egerstedt Magnus
|
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| Princeton, : Princeton University Press, c2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Control theoretic splines [[electronic resource] ] : optimal control, statistics, and path planning / / Magnus Egerstedt and Clyde Martin
| Control theoretic splines [[electronic resource] ] : optimal control, statistics, and path planning / / Magnus Egerstedt and Clyde Martin |
| Autore | Egerstedt Magnus |
| Edizione | [Course Book] |
| Pubbl/distr/stampa | Princeton, : Princeton University Press, c2010 |
| Descrizione fisica | 1 online resource (227 p.) |
| Disciplina | 511/.42 |
| Altri autori (Persone) | MartinClyde |
| Collana | Princeton series in applied mathematics |
| Soggetto topico |
Interpolation
Smoothing (Numerical analysis) Smoothing (Statistics) Curve fitting Splines Spline theory |
| Soggetto non controllato |
Accuracy and precision
Affine space Affine variety Algorithm Approximation Arbitrarily large B-spline Banach space Bernstein polynomial Bifurcation theory Big O notation Birkhoff interpolation Boundary value problem Bézier curve Chaos theory Computation Computational problem Condition number Constrained optimization Continuous function (set theory) Continuous function Control function (econometrics) Control theory Controllability Convex optimization Convolution Cubic Hermite spline Data set Derivative Differentiable function Differential equation Dimension (vector space) Directional derivative Discrete mathematics Dynamic programming Equation Estimation Filtering problem (stochastic processes) Gaussian quadrature Gradient descent Gramian matrix Growth curve (statistics) Hermite interpolation Hermite polynomials Hilbert projection theorem Hilbert space Initial condition Initial value problem Integral equation Iterative method Karush–Kuhn–Tucker conditions Kernel method Lagrange polynomial Law of large numbers Least squares Linear algebra Linear combination Linear filter Linear map Mathematical optimization Mathematics Maxima and minima Monotonic function Nonlinear programming Nonlinear system Normal distribution Numerical analysis Numerical stability Optimal control Optimization problem Ordinary differential equation Orthogonal polynomials Parameter Piecewise Pointwise Polynomial interpolation Polynomial Probability distribution Quadratic programming Random variable Rate of convergence Ratio test Riccati equation Simpson's rule Simultaneous equations Smoothing spline Smoothing Smoothness Special case Spline (mathematics) Spline interpolation Statistic Stochastic calculus Stochastic Telemetry Theorem Trapezoidal rule Waypoint Weight function Without loss of generality |
| ISBN |
1-282-45796-9
1-282-93606-9 9786612936067 9786612457968 1-4008-3387-6 |
| Classificazione | SK 880 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Contents -- Preface -- Chapter One. Introduction -- Chapter Two. Control Systems and Minimum Norm Problems -- Chapter Three. Eight Fundamental Problems -- Chapter Four. Smoothing Splines and Generalizations -- Chapter Five. Approximations and Limiting Concepts -- Chapter Six. Smoothing Splines with Continuous Data -- Chapter Seven. Monotone Smoothing Splines -- Chapter Eight. Smoothing Splines as Integral Filters -- Chapter Nine. Optimal Transfer between Affine Varieties -- Chapter Ten. Path Planning and Telemetry -- Chapter Eleven. Node Selection -- Bibliography -- Index |
| Record Nr. | UNINA-9910780863803321 |
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Egerstedt Magnus
|
||
| Princeton, : Princeton University Press, c2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Control theoretic splines : optimal control, statistics, and path planning / / Magnus Egerstedt and Clyde Martin
| Control theoretic splines : optimal control, statistics, and path planning / / Magnus Egerstedt and Clyde Martin |
| Autore | Egerstedt Magnus |
| Edizione | [Course Book] |
| Pubbl/distr/stampa | Princeton, : Princeton University Press, c2010 |
| Descrizione fisica | 1 online resource (227 p.) |
| Disciplina | 511/.42 |
| Altri autori (Persone) | MartinClyde |
| Collana | Princeton series in applied mathematics |
| Soggetto topico |
Interpolation
Smoothing (Numerical analysis) Smoothing (Statistics) Curve fitting Splines Spline theory |
| ISBN |
1-282-45796-9
1-282-93606-9 9786612936067 9786612457968 1-4008-3387-6 |
| Classificazione | SK 880 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Contents -- Preface -- Chapter One. Introduction -- Chapter Two. Control Systems and Minimum Norm Problems -- Chapter Three. Eight Fundamental Problems -- Chapter Four. Smoothing Splines and Generalizations -- Chapter Five. Approximations and Limiting Concepts -- Chapter Six. Smoothing Splines with Continuous Data -- Chapter Seven. Monotone Smoothing Splines -- Chapter Eight. Smoothing Splines as Integral Filters -- Chapter Nine. Optimal Transfer between Affine Varieties -- Chapter Ten. Path Planning and Telemetry -- Chapter Eleven. Node Selection -- Bibliography -- Index |
| Record Nr. | UNINA-9910807652503321 |
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Egerstedt Magnus
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||
| Princeton, : Princeton University Press, c2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Kernel smoothing : principles, methods and applications / / Sucharita Ghosh
| Kernel smoothing : principles, methods and applications / / Sucharita Ghosh |
| Autore | Ghosh S (Sucharita) |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2018 |
| Descrizione fisica | 1 online resource (1 volume) : illustrations |
| Disciplina | 511/.42 |
| Collana | THEi Wiley ebooks |
| Soggetto topico |
Smoothing (Statistics)
Kernel functions |
| ISBN |
1-118-89050-7
1-118-89037-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Density estimation -- Nonparametric regression -- Trend estimation -- Semiparametric regression -- Surface estimation. |
| Record Nr. | UNINA-9910271040703321 |
|
Ghosh S (Sucharita)
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| Hoboken, New Jersey : , : Wiley, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Kernel smoothing : principles, methods and applications / / Sucharita Ghosh
| Kernel smoothing : principles, methods and applications / / Sucharita Ghosh |
| Autore | Ghosh S (Sucharita) |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2018 |
| Descrizione fisica | 1 online resource (1 volume) : illustrations |
| Disciplina | 511/.42 |
| Collana | THEi Wiley ebooks |
| Soggetto topico |
Smoothing (Statistics)
Kernel functions |
| ISBN |
1-118-89050-7
1-118-89037-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Density estimation -- Nonparametric regression -- Trend estimation -- Semiparametric regression -- Surface estimation. |
| Record Nr. | UNINA-9910824925703321 |
|
Ghosh S (Sucharita)
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| Hoboken, New Jersey : , : Wiley, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Kernel smoothing in MATLAB : theory and practice of kernel smoothing / / Ivanka Horova, Jan Kolacek, Jiri Zelinka
| Kernel smoothing in MATLAB : theory and practice of kernel smoothing / / Ivanka Horova, Jan Kolacek, Jiri Zelinka |
| Autore | Horova Ivanka |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore ; ; Hackensack, NJ, : World Scientific, 2012 |
| Descrizione fisica | 1 online resource (242 p.) |
| Disciplina | 519.5 |
| Altri autori (Persone) |
KolacekJan
ZelinkaJiri |
| Soggetto topico |
Smoothing (Statistics)
Kernel functions |
| ISBN |
1-283-63596-8
981-4405-49-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface; 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; 2.4.1 Reference rule; 2.4.2 Maximal smoothing principle; 2.4.3 Cross-validation methods; 2.4.4 Plug-in method; 2.4.5 Iterative method; 2.5 Density derivative estimation; 2.5.1 Choosing the bandwidth; 2.6 Automatic procedure for simultaneous choice of the kernel, the bandwidth and the kernel order
2.7 Boundary effects2.7.1 Generalized reflection method; 2.8 Simulations; 2.9 Application to real data; 2.9.1 Buffalo snowfall data; 2.9.2 Concentration of cholesterol; 2.10 Use of MATLAB toolbox; 2.10.1 Running the program; 2.10.2 Main figure; 2.10.3 Setting the parameters; 2.10.4 Eye-control method; 2.10.5 The final estimation; 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; 3.3.1 Cross-validation methods; 3.3.2 Maximal smoothing principle; 3.3.3 Plug-in methods 3.3.4 Iterative method3.4 Boundary effects; 3.4.1 Generalized reflection method; 3.5 Application to data; 3.6 Simulations; 3.7 Application to real data; 3.7.1 Trout PCB data; 3.8 Use of MATLAB toolbox; 3.8.1 Running the program; 3.8.2 Main figure; 3.8.3 Setting the parameters; 3.8.4 Eye-control method; 3.8.5 The final estimation; 3.9 Complements; 4. Kernel estimation and reliability assessment; 4.1 Basic Definition; 4.2 Estimation of ROC curves; 4.2.1 Binormal model; 4.2.2 Nonparametric estimates; 4.3 Summary indices based on the ROC curve; 4.3.1 Area under the ROC curve 4.3.2 Maximum improvement of sensitivity over chance diagonal (MIS)4.4 Other indices of reliability assessment; 4.4.1 Cumulative Lift; 4.4.2 Lift Ratio; 4.4.3 Integrated Relative Lift; 4.4.4 Information Value; 4.4.5 KR index; 4.5 Application to real data; 4.5.1 Head trauma data; 4.5.2 Pancreatic cancer data; 4.5.3 Consumer loans data; 4.6 Use of MATLAB toolbox; 4.6.1 Running the program; 4.6.2 Start menu; 4.6.3 Simulation menu; 4.6.4 The final estimation; 5. Kernel estimation of a hazard function; 5.1 Basic definition; 5.2 Statistical properties of the estimate; 5.3 Choosing the bandwidth 5.3.1 Cross-validation method5.3.2 Maximum likelihood method; 5.3.3 Iterative method; 5.3.4 Acceptable bandwidths; 5.3.5 Points of the most rapid change; 5.4 Description of algorithm; 5.5 Application to real data; 5.5.1 Breast carcinoma data; 5.5.2 Cervix carcinoma data; 5.5.3 Chronic lymphocytic leukaemia; 5.5.4 Bone marrow transplant; 5.6 Use of MATLAB toolbox; 5.6.1 Running the program; 5.6.2 Main figure; 5.6.3 Setting the parameters; 5.6.4 Eye-control method; 5.6.5 The final estimation; 5.7 Complements; Simulation of lifetimes; Simulation of censoring times 6. Kernel estimation of a regression function |
| Record Nr. | UNINA-9910807062103321 |
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Horova Ivanka
|
||
| Singapore ; ; Hackensack, NJ, : World Scientific, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Kernel smoothing in MATLAB [[electronic resource] ] : theory and practice of kernel smoothing / / Ivanka Horová, Jan Koláček, Jiří Zelinka
| Kernel smoothing in MATLAB [[electronic resource] ] : theory and practice of kernel smoothing / / Ivanka Horová, Jan Koláček, Jiří Zelinka |
| Autore | Horová Ivanka |
| Pubbl/distr/stampa | Singapore ; ; Hackensack, NJ, : World Scientific, 2012 |
| Descrizione fisica | 1 online resource (242 p.) |
| Disciplina | 519.5 |
| Altri autori (Persone) |
KoláčekJan
ZelinkaJiří |
| Soggetto topico |
Smoothing (Statistics)
Kernel functions |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-283-63596-8
981-4405-49-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface; 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; 2.4.1 Reference rule; 2.4.2 Maximal smoothing principle; 2.4.3 Cross-validation methods; 2.4.4 Plug-in method; 2.4.5 Iterative method; 2.5 Density derivative estimation; 2.5.1 Choosing the bandwidth; 2.6 Automatic procedure for simultaneous choice of the kernel, the bandwidth and the kernel order
2.7 Boundary effects2.7.1 Generalized reflection method; 2.8 Simulations; 2.9 Application to real data; 2.9.1 Buffalo snowfall data; 2.9.2 Concentration of cholesterol; 2.10 Use of MATLAB toolbox; 2.10.1 Running the program; 2.10.2 Main figure; 2.10.3 Setting the parameters; 2.10.4 Eye-control method; 2.10.5 The final estimation; 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; 3.3.1 Cross-validation methods; 3.3.2 Maximal smoothing principle; 3.3.3 Plug-in methods 3.3.4 Iterative method3.4 Boundary effects; 3.4.1 Generalized reflection method; 3.5 Application to data; 3.6 Simulations; 3.7 Application to real data; 3.7.1 Trout PCB data; 3.8 Use of MATLAB toolbox; 3.8.1 Running the program; 3.8.2 Main figure; 3.8.3 Setting the parameters; 3.8.4 Eye-control method; 3.8.5 The final estimation; 3.9 Complements; 4. Kernel estimation and reliability assessment; 4.1 Basic Definition; 4.2 Estimation of ROC curves; 4.2.1 Binormal model; 4.2.2 Nonparametric estimates; 4.3 Summary indices based on the ROC curve; 4.3.1 Area under the ROC curve 4.3.2 Maximum improvement of sensitivity over chance diagonal (MIS)4.4 Other indices of reliability assessment; 4.4.1 Cumulative Lift; 4.4.2 Lift Ratio; 4.4.3 Integrated Relative Lift; 4.4.4 Information Value; 4.4.5 KR index; 4.5 Application to real data; 4.5.1 Head trauma data; 4.5.2 Pancreatic cancer data; 4.5.3 Consumer loans data; 4.6 Use of MATLAB toolbox; 4.6.1 Running the program; 4.6.2 Start menu; 4.6.3 Simulation menu; 4.6.4 The final estimation; 5. Kernel estimation of a hazard function; 5.1 Basic definition; 5.2 Statistical properties of the estimate; 5.3 Choosing the bandwidth 5.3.1 Cross-validation method5.3.2 Maximum likelihood method; 5.3.3 Iterative method; 5.3.4 Acceptable bandwidths; 5.3.5 Points of the most rapid change; 5.4 Description of algorithm; 5.5 Application to real data; 5.5.1 Breast carcinoma data; 5.5.2 Cervix carcinoma data; 5.5.3 Chronic lymphocytic leukaemia; 5.5.4 Bone marrow transplant; 5.6 Use of MATLAB toolbox; 5.6.1 Running the program; 5.6.2 Main figure; 5.6.3 Setting the parameters; 5.6.4 Eye-control method; 5.6.5 The final estimation; 5.7 Complements; Simulation of lifetimes; Simulation of censoring times 6. Kernel estimation of a regression function |
| Record Nr. | UNINA-9910461809203321 |
|
Horová Ivanka
|
||
| Singapore ; ; Hackensack, NJ, : World Scientific, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Kernel smoothing in MATLAB [[electronic resource] ] : theory and practice of kernel smoothing / / Ivanka Horová, Jan Koláček, Jiří Zelinka
| Kernel smoothing in MATLAB [[electronic resource] ] : theory and practice of kernel smoothing / / Ivanka Horová, Jan Koláček, Jiří Zelinka |
| Autore | Horová Ivanka |
| Pubbl/distr/stampa | Singapore ; ; Hackensack, NJ, : World Scientific, 2012 |
| Descrizione fisica | 1 online resource (242 p.) |
| Disciplina | 519.5 |
| Altri autori (Persone) |
KoláčekJan
ZelinkaJiří |
| Soggetto topico |
Smoothing (Statistics)
Kernel functions |
| ISBN |
1-283-63596-8
981-4405-49-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface; 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; 2.4.1 Reference rule; 2.4.2 Maximal smoothing principle; 2.4.3 Cross-validation methods; 2.4.4 Plug-in method; 2.4.5 Iterative method; 2.5 Density derivative estimation; 2.5.1 Choosing the bandwidth; 2.6 Automatic procedure for simultaneous choice of the kernel, the bandwidth and the kernel order
2.7 Boundary effects2.7.1 Generalized reflection method; 2.8 Simulations; 2.9 Application to real data; 2.9.1 Buffalo snowfall data; 2.9.2 Concentration of cholesterol; 2.10 Use of MATLAB toolbox; 2.10.1 Running the program; 2.10.2 Main figure; 2.10.3 Setting the parameters; 2.10.4 Eye-control method; 2.10.5 The final estimation; 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; 3.3.1 Cross-validation methods; 3.3.2 Maximal smoothing principle; 3.3.3 Plug-in methods 3.3.4 Iterative method3.4 Boundary effects; 3.4.1 Generalized reflection method; 3.5 Application to data; 3.6 Simulations; 3.7 Application to real data; 3.7.1 Trout PCB data; 3.8 Use of MATLAB toolbox; 3.8.1 Running the program; 3.8.2 Main figure; 3.8.3 Setting the parameters; 3.8.4 Eye-control method; 3.8.5 The final estimation; 3.9 Complements; 4. Kernel estimation and reliability assessment; 4.1 Basic Definition; 4.2 Estimation of ROC curves; 4.2.1 Binormal model; 4.2.2 Nonparametric estimates; 4.3 Summary indices based on the ROC curve; 4.3.1 Area under the ROC curve 4.3.2 Maximum improvement of sensitivity over chance diagonal (MIS)4.4 Other indices of reliability assessment; 4.4.1 Cumulative Lift; 4.4.2 Lift Ratio; 4.4.3 Integrated Relative Lift; 4.4.4 Information Value; 4.4.5 KR index; 4.5 Application to real data; 4.5.1 Head trauma data; 4.5.2 Pancreatic cancer data; 4.5.3 Consumer loans data; 4.6 Use of MATLAB toolbox; 4.6.1 Running the program; 4.6.2 Start menu; 4.6.3 Simulation menu; 4.6.4 The final estimation; 5. Kernel estimation of a hazard function; 5.1 Basic definition; 5.2 Statistical properties of the estimate; 5.3 Choosing the bandwidth 5.3.1 Cross-validation method5.3.2 Maximum likelihood method; 5.3.3 Iterative method; 5.3.4 Acceptable bandwidths; 5.3.5 Points of the most rapid change; 5.4 Description of algorithm; 5.5 Application to real data; 5.5.1 Breast carcinoma data; 5.5.2 Cervix carcinoma data; 5.5.3 Chronic lymphocytic leukaemia; 5.5.4 Bone marrow transplant; 5.6 Use of MATLAB toolbox; 5.6.1 Running the program; 5.6.2 Main figure; 5.6.3 Setting the parameters; 5.6.4 Eye-control method; 5.6.5 The final estimation; 5.7 Complements; Simulation of lifetimes; Simulation of censoring times 6. Kernel estimation of a regression function |
| Record Nr. | UNINA-9910785918803321 |
|
Horová Ivanka
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||
| Singapore ; ; Hackensack, NJ, : World Scientific, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Nonparametric and semiparametric models / Wolfgang Härdle ... [et al.]
| Nonparametric and semiparametric models / Wolfgang Härdle ... [et al.] |
| Pubbl/distr/stampa | Berlin ; New York : Springer, c2004 |
| Descrizione fisica | xxvii, 299 p. ; 24 cm |
| Disciplina | 519.54 |
| Altri autori (Persone) | Härdle, Wolfgang Karlauthor |
| Collana | Springer series in statistics |
| Soggetto topico |
Nonparametric statistics
Mathematical models Smoothing (Statistics) |
| ISBN | 3540207228 |
| Classificazione | AMS 62G |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | und |
| Record Nr. | UNISALENTO-991002610379707536 |
| Berlin ; New York : Springer, c2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
| ||
Smoothing and regression [[electronic resource] ] : approaches, computation, and application / / edited by Michael G. Schimek
| Smoothing and regression [[electronic resource] ] : approaches, computation, and application / / edited by Michael G. Schimek |
| Pubbl/distr/stampa | New York, : Wiley, 2000 |
| Descrizione fisica | 1 online resource (648 p.) |
| Disciplina |
519.5/36
519.536 |
| Altri autori (Persone) | SchimekMichael G |
| Collana | Wiley series in probability and mathematical statistics. Applied probability and statistics section |
| Soggetto topico |
Smoothing (Statistics)
Nonparametric statistics Regression analysis |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-283-44611-1
9786613446114 1-118-15065-1 1-118-15064-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Smoothing and Regression: Approaches, Computation, and Application; Contents; Foreword; Preface; 1. Spline Regression; 1.1 Introduction; 1.2 General Form of the Estimator; 1.3 The Linear Smoothing Spline; 1.4 Large-Sample Efficiency; 1.5 Bayesian Motivation; 1.6 Extensions and Implementations; References; 2. Variance Estimation and Smoothing-Parameter Selection for Spline Regression; 2.1 Introduction and Some Definitions; 2.2 Interpretation of the Smoothing Parameter; 2.3 Quantifying the Complexity of a Smoothing Spline; 2.4 Estimation of σ2; 2.5 Determination of λ; 2.6 Estimation of τ2
4.2 Nonparametric Variance Estimators4.3 Bandwidth Choice for Kernel Regression Estimators; References; 5. Spline and Kernel Regression under Shape Restrictions; 5.1 Introduction; 5.2 Description of the Main Methods; 5.3 A Comparative View; 5.4 Examples; 5.5 Software Hints; References; 6. Spline and Kernel Regression for Dependent Data; 6.1 Introduction; 6.2 Approaches for a Known Autocorrelation Function; 6.3 Approaches for an Unknown Autocorrelation Function; 6.4 A Bayesian Approach to Smoothing Dependent Data; 6.5 Applications of Smoothing Dependent Data; References 7. Wavelets for Regression and Other Statistical Problems7.1 Introduction; 7.2 Wavelet Expansions; 7.3 The Discrete Wavelet Transform in S; 7.4 Wavelet Shrinkage; 7.5 Estimators for Data With Correlated Noise; 7.6 Implementation of the Wavelet Transform; 7.7 How to Obtain and Install the Wavelet Software; References; 8. Smoothing Methods for Discrete Data; 8.1 Introduction; 8.2 Smoothing Contingency Tables; 8.3 Smoothing Approaches to Categorical Regression; 8.4 Conclusion; References; 9. Local Polynomial Fitting; 9.1 Introduction; 9.2 Properties of Local Polynomial Fitting 9.3 Choice of Bandwidth9.4 Choice of the Degree; 9.5 Local Modeling; 9.6 Some More Applications; References; 10. Additive and Generalized Additive Models; 10.1 Introduction; 10.2 The Additive Model; 10.3 Generalized Additive Models; 10.4 Alternating Conditional Expectations Additivity, and Variance Stabilization; 10.5 Smoothing Parameter and Bandwidth Determination; 10.6 Model Diagnostics; 10.7 New Developments; References; 11. Multivariate Spline Regression; 11.1 Introduction; 11.2 Smoothing Splines as Bayes Estimates; 11.3 ANOVA Decomposition on Product Domains; 11.4 Tensor Product Splines 11.5 Computation |
| Record Nr. | UNINA-9910139720503321 |
| New York, : Wiley, 2000 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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