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Local Regression and Likelihood

Local Regression and Likelihood
Author: Clive Loader
Publisher: Springer Science & Business Media
Total Pages: 290
Release: 2006-05-09
Genre: Mathematics
ISBN: 0387227326

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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 Modelling and Its Applications

Local Polynomial Modelling and Its Applications
Author: Jianqing Fan
Publisher: Routledge
Total Pages: 362
Release: 2018-05-02
Genre: Mathematics
ISBN: 1351434802

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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.


Statistical Theory and Computational Aspects of Smoothing

Statistical Theory and Computational Aspects of Smoothing
Author: Wolfgang Härdle
Publisher: Springer Science & Business Media
Total Pages: 265
Release: 2013-03-08
Genre: Business & Economics
ISBN: 3642484255

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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.


Maximum Penalized Likelihood Estimation

Maximum Penalized Likelihood Estimation
Author: Paul P. Eggermont
Publisher: Springer Science & Business Media
Total Pages: 580
Release: 2009-06-02
Genre: Mathematics
ISBN: 0387689028

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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.


Local Polynomial Modelling and Its Applications

Local Polynomial Modelling and Its Applications
Author: Jianqing Fan
Publisher: Routledge
Total Pages: 358
Release: 2018-05-02
Genre: Mathematics
ISBN: 1351434810

Download Local Polynomial Modelling and Its Applications Book in PDF, ePub and Kindle

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.