Author: Paul P. Eggermont
Publisher: Springer Science & Business Media
ISBN: 0387689028
Category : Mathematics
Languages : en
Pages : 580
Book Description
Unique blend of asymptotic theory and small sample practice through simulation experiments and data analysis. Novel reproducing kernel Hilbert space methods for the analysis of smoothing splines and local polynomials. Leading to uniform error bounds and honest confidence bands for the mean function using smoothing splines Exhaustive exposition of algorithms, including the Kalman filter, for the computation of smoothing splines of arbitrary order.
Maximum Penalized Likelihood Estimation
Author: Paul P. Eggermont
Publisher: Springer Science & Business Media
ISBN: 0387689028
Category : Mathematics
Languages : en
Pages : 580
Book Description
Unique blend of asymptotic theory and small sample practice through simulation experiments and data analysis. Novel reproducing kernel Hilbert space methods for the analysis of smoothing splines and local polynomials. Leading to uniform error bounds and honest confidence bands for the mean function using smoothing splines Exhaustive exposition of algorithms, including the Kalman filter, for the computation of smoothing splines of arbitrary order.
Publisher: Springer Science & Business Media
ISBN: 0387689028
Category : Mathematics
Languages : en
Pages : 580
Book Description
Unique blend of asymptotic theory and small sample practice through simulation experiments and data analysis. Novel reproducing kernel Hilbert space methods for the analysis of smoothing splines and local polynomials. Leading to uniform error bounds and honest confidence bands for the mean function using smoothing splines Exhaustive exposition of algorithms, including the Kalman filter, for the computation of smoothing splines of arbitrary order.
Maximum Penalized Likelihood Estimation
Author: P.P.B. Eggermont
Publisher: Springer Nature
ISBN: 1071612441
Category : Mathematics
Languages : en
Pages : 514
Book Description
This book deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints. The focal points are existence and uniqueness of the estimators, almost sure convergence rates for the L1 error, and data-driven smoothing parameter selection methods, including their practical performance. The reader will gain insight into technical tools from probability theory and applied mathematics.
Publisher: Springer Nature
ISBN: 1071612441
Category : Mathematics
Languages : en
Pages : 514
Book Description
This book deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints. The focal points are existence and uniqueness of the estimators, almost sure convergence rates for the L1 error, and data-driven smoothing parameter selection methods, including their practical performance. The reader will gain insight into technical tools from probability theory and applied mathematics.
Maximum Penalized Likelihood Estimation: Regression
Author: Paulus Petrus Bernardus Eggermont
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages :
Book Description
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages :
Book Description
Maximum Penalized Likelihood Estimation
Author: P.P.B. Eggermont
Publisher: Springer
ISBN: 9780387952680
Category : Mathematics
Languages : en
Pages : 0
Book Description
This book deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints. The focal points are existence and uniqueness of the estimators, almost sure convergence rates for the L1 error, and data-driven smoothing parameter selection methods, including their practical performance. The reader will gain insight into technical tools from probability theory and applied mathematics.
Publisher: Springer
ISBN: 9780387952680
Category : Mathematics
Languages : en
Pages : 0
Book Description
This book deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints. The focal points are existence and uniqueness of the estimators, almost sure convergence rates for the L1 error, and data-driven smoothing parameter selection methods, including their practical performance. The reader will gain insight into technical tools from probability theory and applied mathematics.
Adaptive Maximum Penalized Likelihood Estimation
Author: Tyler Shumway
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 32
Book Description
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 32
Book Description
Maximum Penalized Likelihood Estimation
Author: P.P.B. Eggermont
Publisher: Springer
ISBN: 9780387952680
Category : Mathematics
Languages : en
Pages : 512
Book Description
This book deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints. The focal points are existence and uniqueness of the estimators, almost sure convergence rates for the L1 error, and data-driven smoothing parameter selection methods, including their practical performance. The reader will gain insight into technical tools from probability theory and applied mathematics.
Publisher: Springer
ISBN: 9780387952680
Category : Mathematics
Languages : en
Pages : 512
Book Description
This book deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints. The focal points are existence and uniqueness of the estimators, almost sure convergence rates for the L1 error, and data-driven smoothing parameter selection methods, including their practical performance. The reader will gain insight into technical tools from probability theory and applied mathematics.
Maximum Penalized Likelihood Estimation
Author: P.P.B. Eggermont
Publisher: Springer
ISBN: 9780387952680
Category : Mathematics
Languages : en
Pages : 512
Book Description
This book deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints. The focal points are existence and uniqueness of the estimators, almost sure convergence rates for the L1 error, and data-driven smoothing parameter selection methods, including their practical performance. The reader will gain insight into technical tools from probability theory and applied mathematics.
Publisher: Springer
ISBN: 9780387952680
Category : Mathematics
Languages : en
Pages : 512
Book Description
This book deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints. The focal points are existence and uniqueness of the estimators, almost sure convergence rates for the L1 error, and data-driven smoothing parameter selection methods, including their practical performance. The reader will gain insight into technical tools from probability theory and applied mathematics.
Maximum Penalized Likelihood Estimation
Author: P.P.B. Eggermont
Publisher: Springer
ISBN: 9781441929280
Category : Mathematics
Languages : en
Pages : 512
Book Description
This book deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints. The focal points are existence and uniqueness of the estimators, almost sure convergence rates for the L1 error, and data-driven smoothing parameter selection methods, including their practical performance. The reader will gain insight into technical tools from probability theory and applied mathematics.
Publisher: Springer
ISBN: 9781441929280
Category : Mathematics
Languages : en
Pages : 512
Book Description
This book deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints. The focal points are existence and uniqueness of the estimators, almost sure convergence rates for the L1 error, and data-driven smoothing parameter selection methods, including their practical performance. The reader will gain insight into technical tools from probability theory and applied mathematics.
Maximum Penalized Likelihood Estimation
Author: Shih-hung Yü
Publisher:
ISBN:
Category : Algorithms
Languages : en
Pages : 206
Book Description
Publisher:
ISBN:
Category : Algorithms
Languages : en
Pages : 206
Book Description
Nonparametric Maximum Penalized Likelihood Estimation of Lifetime Density Functions
Author: André Michelle Lubecke
Publisher:
ISBN:
Category :
Languages : en
Pages : 140
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 140
Book Description