Author: Clive Loader
Publisher: Springer Science & Business Media
ISBN: 0387227326
Category : Mathematics
Languages : en
Pages : 290
Book Description
Separation of signal from noise is the most fundamental problem in data analysis, arising in such fields as: signal processing, econometrics, actuarial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, with extensions to local likelihood and density estimation. Practical information is also included on how to implement these methods in the programs S-PLUS and LOCFIT.
Local Regression and Likelihood
Author: Clive Loader
Publisher: Springer Science & Business Media
ISBN: 0387227326
Category : Mathematics
Languages : en
Pages : 290
Book Description
Separation of signal from noise is the most fundamental problem in data analysis, arising in such fields as: signal processing, econometrics, actuarial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, with extensions to local likelihood and density estimation. Practical information is also included on how to implement these methods in the programs S-PLUS and LOCFIT.
Publisher: Springer Science & Business Media
ISBN: 0387227326
Category : Mathematics
Languages : en
Pages : 290
Book Description
Separation of signal from noise is the most fundamental problem in data analysis, arising in such fields as: signal processing, econometrics, actuarial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, with extensions to local likelihood and density estimation. Practical information is also included on how to implement these methods in the programs S-PLUS and LOCFIT.
Local Polynomial Kernel Regression for Generalized Linear Models and Quasi-likelihood Functions
Author: Jianqing Fan
Publisher:
ISBN:
Category : Linear models (Statistics)
Languages : en
Pages : 23
Book Description
Publisher:
ISBN:
Category : Linear models (Statistics)
Languages : en
Pages : 23
Book Description
Local Fitting of Regression Models by Likelihood
Author: Nils Lid Hjort
Publisher:
ISBN: 9788255309352
Category :
Languages : en
Pages : 16
Book Description
Publisher:
ISBN: 9788255309352
Category :
Languages : en
Pages : 16
Book Description
Local Polynomial Modelling and Its Applications
Author: Jianqing Fan
Publisher: Routledge
ISBN: 1351434810
Category : Mathematics
Languages : en
Pages : 358
Book Description
Data-analytic approaches to regression problems, arising from many scientific disciplines are described in this book. The aim of these nonparametric methods is to relax assumptions on the form of a regression function and to let data search for a suitable function that describes the data well. The use of these nonparametric functions with parametric techniques can yield very powerful data analysis tools. Local polynomial modeling and its applications provides an up-to-date picture on state-of-the-art nonparametric regression techniques. The emphasis of the book is on methodologies rather than on theory, with a particular focus on applications of nonparametric techniques to various statistical problems. High-dimensional data-analytic tools are presented, and the book includes a variety of examples. This will be a valuable reference for research and applied statisticians, and will serve as a textbook for graduate students and others interested in nonparametric regression.
Publisher: Routledge
ISBN: 1351434810
Category : Mathematics
Languages : en
Pages : 358
Book Description
Data-analytic approaches to regression problems, arising from many scientific disciplines are described in this book. The aim of these nonparametric methods is to relax assumptions on the form of a regression function and to let data search for a suitable function that describes the data well. The use of these nonparametric functions with parametric techniques can yield very powerful data analysis tools. Local polynomial modeling and its applications provides an up-to-date picture on state-of-the-art nonparametric regression techniques. The emphasis of the book is on methodologies rather than on theory, with a particular focus on applications of nonparametric techniques to various statistical problems. High-dimensional data-analytic tools are presented, and the book includes a variety of examples. This will be a valuable reference for research and applied statisticians, and will serve as a textbook for graduate students and others interested in nonparametric regression.
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.
A Simple Local Least Squares Approach for Estimating the Regression Function of Binary Response Data and Related Data-driven Bandwidth Selection Procedures
Author: Aaron Kenji Aragaki
Publisher:
ISBN:
Category :
Languages : en
Pages : 336
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 336
Book Description
Statistical Theory and Computational Aspects of Smoothing
Author: Wolfgang Härdle
Publisher: Springer Science & Business Media
ISBN: 3642484255
Category : Business & Economics
Languages : en
Pages : 265
Book Description
One of the main applications of statistical smoothing techniques is nonparametric regression. For the last 15 years there has been a strong theoretical interest in the development of such techniques. Related algorithmic concepts have been a main concern in computational statistics. Smoothing techniques in regression as well as other statistical methods are increasingly applied in biosciences and economics. But they are also relevant for medical and psychological research. Introduced are new developments in scatterplot smoothing and applications in statistical modelling. The treatment of the topics is on an intermediate level avoiding too much technicalities. Computational and applied aspects are considered throughout. Of particular interest to readers is the discussion of recent local fitting techniques.
Publisher: Springer Science & Business Media
ISBN: 3642484255
Category : Business & Economics
Languages : en
Pages : 265
Book Description
One of the main applications of statistical smoothing techniques is nonparametric regression. For the last 15 years there has been a strong theoretical interest in the development of such techniques. Related algorithmic concepts have been a main concern in computational statistics. Smoothing techniques in regression as well as other statistical methods are increasingly applied in biosciences and economics. But they are also relevant for medical and psychological research. Introduced are new developments in scatterplot smoothing and applications in statistical modelling. The treatment of the topics is on an intermediate level avoiding too much technicalities. Computational and applied aspects are considered throughout. Of particular interest to readers is the discussion of recent local fitting techniques.
Local Likelihood and Local Partial Likelihood in Hazard Regression
Author: Martin S. King
Publisher:
ISBN:
Category : Regression analysis
Languages : en
Pages : 97
Book Description
Publisher:
ISBN:
Category : Regression analysis
Languages : en
Pages : 97
Book Description
On Maximum Likelihood Estimation for Two-phase Linear Regression
Author: David Luther Sylwester
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 172
Book Description
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 172
Book Description
Local Likelihood for Non-Parametric Arch(1) Models
Author: Francesco Audrino
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
We propose a local likelihood estimation for the log-transformed ARCH(1) model in the financial field. Our nonparametric estimator is constructed within the likelihood framework for non-Gaussian observations: It is different from standard kernel regression smoothing, where the innovations are assumed to be normally distributed. We derive consistency and asymptotic normality for our estimators and conclude from simulation and real data analysis that the local likelihood estimator has better predictive potential than classical local regression.
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
We propose a local likelihood estimation for the log-transformed ARCH(1) model in the financial field. Our nonparametric estimator is constructed within the likelihood framework for non-Gaussian observations: It is different from standard kernel regression smoothing, where the innovations are assumed to be normally distributed. We derive consistency and asymptotic normality for our estimators and conclude from simulation and real data analysis that the local likelihood estimator has better predictive potential than classical local regression.