Last edited by Kajizragore
Monday, July 27, 2020 | History

7 edition of Nonparametric smoothing and lack-of-fit tests found in the catalog.

Nonparametric smoothing and lack-of-fit tests

by Jeffrey D. Hart

  • 181 Want to read
  • 31 Currently reading

Published by Springer in New York .
Written in English

    Subjects:
  • Smoothing (Statistics),
  • Nonparametric statistics.,
  • Goodness-of-fit tests.

  • Edition Notes

    Includes bibliographical references (p. 271-279) and indexes.

    StatementJeffrey D. Hart.
    SeriesSpringer series in statistics
    Classifications
    LC ClassificationsQA278 .H357 1997
    The Physical Object
    Paginationxii, 287 p. :
    Number of Pages287
    ID Numbers
    Open LibraryOL665310M
    ISBN 100387949801
    LC Control Number97010931

    We developed two kernel smoothing based tests of a parametric mean-regression model against a nonparametric alternative when the response variable is right-censored. The new test statistics are inspired by the synthetic data and the weighted least squares approaches for estimating the parameters of a (non)linear regression model under by: H¨ardle: Smoothing Techniques: With Implementation in S Harrell: Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis Hart: Nonparametric Smoothing and Lack-of-Fit Tests Hastie/Tibshirani/Friedman: The Elements of Statistical Learning: Data Mining, Inference, and Prediction.

    The new edition deletes most of the asymptotic theory for smoothing splines and smoothing spline variants, and adds order selection for hierarchical models, estimation in partially linear models, polynomial-trigonometric regression, new results on bandwidth selection, and locally linear regression. The first edition was published as (). Hdrdle: Smoothing Techniques: With Implementation in S. Hart: Nonparametric Smoothing and Lack-of-Fit Tests. Hartigan: Bayes Theory. Hedayat/Sloane/Stufken: Orthogonal Arrays: Theory and Applications. Heyde: Quasi-Likelihood and its Application: A General Approach to Optimal Parameter Estimation. Heyer: Theory of Statistical Experiments.

    Härdle: Smoothing Techniques: With Implementation in S Harrell: Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis Hart: Nonparametric Smoothing and Lack-of-Fit Tests Hastie/Tibshirani/Friedman: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Gu: Smoothing Spline ANOVA Models Regression Haberman: Advanced Statistics, Volume I: Description of Populations Hall: The Bootstrap and Edgeworth Expansion Härdle: Smoothing Techniques: With Implementation in S Logistic Regression, and Survival Analysis Hart: Nonparametric Smoothing and Lack-of-Fit Tests Inference, and Prediction.


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Nonparametric smoothing and lack-of-fit tests by Jeffrey D. Hart Download PDF EPUB FB2

The The primary primary aim aim of of this this book book is is to to explore explore the the use use of of nonparametric nonparametric regres­ regres­ sion sion (i. e., (i. e., smoothing) smoothing) methodology methodology in in testing testing the the fit fit of of parametric Brand: Springer-Verlag New York.

The book reviews many of the existing methods for testing lack-of-fit and also proposes a number of new methods, addressing both applied and theoretical aspects of the model checking problems.

As such, the book is of interest to practitioners of statistics and researchers investigating either lack-of-fit tests or nonparametric smoothing by: The The primary primary aim aim of of this this book book is is to to explore explore the the use use of of nonparametric nonparametric regres­ regres­ sion sion (i.

e., (i. e., smoothing) smoothing) methodology methodology in in testing testing the the fit fit of of parametric. Some Basic Ideas of Smoothing Statistical Properties of Smoothers Data-Driven Choice of Smoothing Parameters Classical Lack-of-Fit Tests Lack-of-Fit Tests Based on Linear Smoothers Testing for Association via Automated Order Selection Data-Driven Lack-of-Fit Tests for General Parametric Models Get this from a library.

Nonparametric Smoothing and Lack-of-Fit Tests. [Jeffrey D Hart] -- The The primary primary aim aim of of this this book book is is to to explore explore the the use use of of nonparametric nonparametric regres regres sion sion (i. e., (i. e., smoothing) smoothing).

Find helpful customer reviews and review ratings for Nonparametric Smoothing and Lack-of-Fit Tests (Springer Series in Statistics) at Read honest and 5/5. SASEr Implementations of Nonparametric Smoothing and Lack-of-Fit Tests Based on Smoothers George F.

von Borries, Texas A&M University, College Station, TX Jeffrey D. Hart, Texas A&M University, College Station, TX 1 Abstract A user-friendly program in SASsr is proposed to perform many nonparametric smoothing and test-ing procedures.

The book reviews many of the existing methods for testing lack-of-fit and also proposes a number of new methods, addressing both applied and theoretical aspects of the model checking problems.

As such, the book is of interest to practitioners of statistics and researchers investigating either lack-of-fit tests or nonparametric smoothing ideas. Find many great new & used options and get the best deals for Springer Series in Statistics: Nonparametric Smoothing and Lack-of-Fit Tests by Jeffrey D.

Hart (, Hardcover) at the best online prices at eBay. Free shipping for many products. Abstract. In this chapter we consider testing the fit of parametric models of a more general nature than the constant mean model of Chapter 7. We begin with the case of a linear model, i.e., the case where r is hypothesized to be a linear combination of known functions.

The fit of such models can be tested by applying the methods of Chapter 7 to : Jeffrey D. Hart. A comparison of the fits given by the smooth and the specified parametric model provides a test for lack of fit.

Such testing methodology is presented by Hardle and Mammen(), Zheng(), Hart. optimization over the smoothing parameter. The method requires that the true variance function is constant.

5 References Cook, R. and Weisberg, S. Applied Regression Including Computing and Graphics. New York: Wiley. Hart, J. Nonparametric Smoothing and Lack-of-Fit Tests.

New York: Springer-Verlag. In recent years a number of authors have studied lack-of-fit tests that make use of nonparametric smoothing ideas.

Some of these tests, such as those proposed by Eubank and Hart (), utilize. This book gives a systematic, comprehensive, and unified account of modern nonparametric statistics of density estimation, nonparametric regression, filtering signals, and time series analysis.

The companion software package, available over the Internet, brings all of the discussed topics into the realm of interactive research. Nonparametric Smoothing and Lack-of-Fit Tests (Springer Series in Statistics) $ Introduction to Nonparametric Statistics for the Biological Sciences Using R.

Downloadable. We develop two kernel smoothing based tests of a parametric mean-regressionmodel against a nonparametric alternative when the response variable is right-censored. The new test statistics are inspired by the synthetic data and the weightedleast squares approaches for estimating the parameters of a (non)linear regressionmodel under censoring.

There has been a great deal of interest in developing lack-of-fit tests for linear or other parametric models, but these tests are concerned mostly with the conditional mean function. Ex-ceptions include the tests of Zheng () and Horowitz and Spokoiny (), both. Errata for Nonparametric Smoothing and Lack-of-Fit Tests p.

6, expression (): Parentheses are missing in the numerator on the right-hand side of the equal sign. The numerator should be. My Book. I've written a book entitled Nonparametric Smoothing and Lack-of-Fit Tests, which has been published by Springer-Verlag. You can find out more about the book and place an order for it via the links below.

Alas, there are a few errors in the book, which you can find out about here. HAL Id: hal Submitted on 1 Apr HAL is a multi-disciplinary open access archive for the deposit and.

Downloadable (with restrictions)! We developed two kernel smoothing based tests of a parametric mean-regression model against a nonparametric alternative when the response variable is right-censored.

The new test statistics are inspired by the synthetic data and the weighted least squares approaches for estimating the parameters of a (non)linear regression model under censoring.Examples include tests of deviation from the parametric linear regression [9, 10].

25 Another method tests the goodness-of- t of a linear model, which can potentially detect a nonparametric form under the alternative [11]. A test that allows a nonparametric form under the null exists [12], but it is only de ned for a model with one Size: KB.Nonparametric lack-of-fit tests for parametric mean-regression models with censored data O.

Lopeza,b, V. Patileab,c a IRMAR, France b CREST-ENSAI, Campus de Ker Lann, Rue Blaise Pascal BPBruz cedex, France c INSA-IRMAR, France a r t i c l e i n f o Article history: Received 18 June Available online 20 April