On Nonparametric Estimation and Inference with Censored Data, Bandwidth Selection for Local Polynomial Regression, and Subset Selection in Explanatory Regression Analyses PDF Download
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Author: Derick Randall Peterson
Publisher:
ISBN:
Category :
Languages : en
Pages : 222
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Book Description
Author: Derick Randall Peterson
Publisher:
ISBN:
Category :
Languages : en
Pages : 222
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Book Description
Author: Mathisca de Gunst
Publisher: IMS
ISBN: 9780940600508
Category : Mathematics
Languages : en
Pages : 660
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Book Description
Author: Jianqing Fan
Publisher: Routledge
ISBN: 1351434802
Category : Mathematics
Languages : en
Pages : 362
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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.
Author: Matias D. Cattaneo
Publisher: Cambridge University Press
ISBN: 1108670423
Category : Political Science
Languages : en
Pages : 114
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Book Description
In this Element and its accompanying second Element, A Practical Introduction to Regression Discontinuity Designs: Extensions, Matias Cattaneo, Nicolás Idrobo, and Rocıìo Titiunik provide an accessible and practical guide for the analysis and interpretation of regression discontinuity (RD) designs that encourages the use of a common set of practices and facilitates the accumulation of RD-based empirical evidence. In this Element, the authors discuss the foundations of the canonical Sharp RD design, which has the following features: (i) the score is continuously distributed and has only one dimension, (ii) there is only one cutoff, and (iii) compliance with the treatment assignment is perfect. In the second Element, the authors discuss practical and conceptual extensions to this basic RD setup.
Author: Alan Miller
Publisher: CRC Press
ISBN: 1420035932
Category : Mathematics
Languages : en
Pages : 258
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Book Description
Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author ha
Author: Stanford University. Dept. of Statistics
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 23
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Book Description
Author: Randall L. Eubank
Publisher: CRC Press
ISBN: 9780824793371
Category : Mathematics
Languages : en
Pages : 368
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Book Description
Provides a unified account of the most popular approaches to nonparametric regression smoothing. This edition contains discussions of boundary corrections for trigonometric series estimators; detailed asymptotics for polynomial regression; testing goodness-of-fit; estimation in partially linear models; practical aspects, problems and methods for confidence intervals and bands; local polynomial regression; and form and asymptotic properties of linear smoothing splines.
Author: Chul-Ki Kim
Publisher:
ISBN:
Category :
Languages : en
Pages : 174
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Book Description
Author: Clive Loader
Publisher: Springer Science & Business Media
ISBN: 0387227326
Category : Mathematics
Languages : en
Pages : 290
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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.
Author: László Györfi
Publisher: Springer Science & Business Media
ISBN: 0387224424
Category : Mathematics
Languages : en
Pages : 662
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Book Description
This book provides a systematic in-depth analysis of nonparametric regression with random design. It covers almost all known estimates. The emphasis is on distribution-free properties of the estimates.
