Skip to main content
Applied Nonparametric Regression
  • Get access
    Check if you have access via personal or institutional login
  • Cited by 753
  • Cited by
    This book has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Citores, Leire Ibaibarriaga, Leire and Jardim, Ernesto 2018. Uncertainty estimation and model selection in stock assessment models with non-parametric effects on fishing mortality. ICES Journal of Marine Science, Vol. 75, Issue. 2, p. 585.

    Moosavi, Seyedeh Robab Qajar, Jafar and Riazi, Masoud 2018. A comparison of methods for denoising of well test pressure data. Journal of Petroleum Exploration and Production Technology,

    Prahutama, Alan Suparti and Utami, Tiani Wahyu 2018. Modelling fourier regression for time series data- a case study: modelling inflation in foods sector in Indonesia. Journal of Physics: Conference Series, Vol. 974, Issue. , p. 012067.

    Duda, Piotr Jaworski, Maciej and Rutkowski, Leszek 2018. Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks. International Journal of Neural Systems, Vol. 28, Issue. 02, p. 1750048.

    Wang, Wenjing and Chen, Xi 2018. An adaptive two-stage dual metamodeling approach for stochastic simulation experiments. IISE Transactions, Vol. 50, Issue. 9, p. 820.

    González-Val, Rafael 2018. Historical urban growth in Europe (1300-1800). Papers in Regional Science,

    Gutiérrez, Andrés Zhang, Hanwen Tellez, Cristian and Guerrero, Stalyn 2018. Calibrated Bayesian shrinkage of finite population totals in survey sampling. Journal of Statistics and Management Systems, Vol. 21, Issue. 2, p. 225.

    Berger, Lukas Kleinheinz, Konstantin Attili, Antonio Bisetti, Fabrizio Pitsch, Heinz and Mueller, Michael E. 2018. Numerically accurate computational techniques for optimal estimator analyses of multi-parameter models. Combustion Theory and Modelling, Vol. 22, Issue. 3, p. 480.

    蒋, 晓艺 2018. N-W Kernel Regression Estimation for Correlation Function of Bivariate Extremes Copula Function. Statistics and Application, Vol. 07, Issue. 02, p. 234.

    Wang, Weina Wu, Chunlin and Deng, Jiansong 2018. A General Selective Averaging Method for Piecewise Constant Signal and Image Processing. Journal of Scientific Computing, Vol. 76, Issue. 2, p. 1078.

    Wheelock, David C. and Wilson, Paul W. 2018. The evolution of scale economies in US banking. Journal of Applied Econometrics, Vol. 33, Issue. 1, p. 16.

    Wilcox, Rand 2018. An inferential method for determining which of two independent variables is most important when there is curvature. Journal of Modern Applied Statistical Methods, Vol. 17, Issue. 1,

    2018. Modern Regression Methods. p. 385.

    Orbe, Jesus and Virto, Jorge 2018. Penalized spline smoothing using Kaplan-Meier weights with censored data. Biometrical Journal,

    Joshi, Rajani R. 2018. Diversity and motif conservation in protein 3D structural landscape: exploration by a new multivariate simulation method. Journal of Molecular Modeling, Vol. 24, Issue. 4,

    Zhao, Xiaobing Wang, Weiwei Liu, Lei and Shih, Ya-Chen T. 2018. A flexible quantile regression model for medical costs with application to Medical Expenditure Panel Survey Study. Statistics in Medicine, Vol. 37, Issue. 17, p. 2645.

    Dey, Tanujit Kim, Kun Ho and Lim, Chae Young 2018. Bayesian time series regression with nonparametric modeling of autocorrelation. Computational Statistics,

    Chesneau, Christophe Doosti, Hassan and Stone, Lewi 2018. Adaptive wavelet estimation of a function from an m-dependent process with possibly unbounded m. Communications in Statistics - Theory and Methods, p. 1.

    Cai, Zongwu Jing, Bingyi Kong, Xinbing and Liu, Zhi 2017. Nonparametric regression with nearly integrated regressors under long-run dependence. The Econometrics Journal, Vol. 20, Issue. 1, p. 118.

    Dou, Hao Ming, Delie Yang, Zhi Pan, Zhihong Li, Yansheng and Tian, Jinwen 2017. Object-Based Visual Saliency via Laplacian Regularized Kernel Regression. IEEE Transactions on Multimedia, Vol. 19, Issue. 8, p. 1718.

  • Export citation
  • Recommend to librarian
  • Recommend this book

    Email your librarian or administrator to recommend adding this book to your organisation's collection.

    Applied Nonparametric Regression
    • Online ISBN: 9781139052139
    • Book DOI:
    Please enter your name
    Please enter a valid email address
    Who would you like to send this to *
  • Buy the print book

Book description

Applied Nonparametric Regression is the first book to bring together in one place the techniques for regression curve smoothing involving more than one variable. The computer and the development of interactive graphics programs have made curve estimation possible. This volume focuses on the applications and practical problems of two central aspects of curve smoothing: the choice of smoothing parameters and the construction of confidence bounds. Härdle argues that all smoothing methods are based on a local averaging mechanism and can be seen as essentially equivalent to kernel smoothing. To simplify the exposition, kernel smoothers are introduced and discussed in great detail. Building on this exposition, various other smoothing methods (among them splines and orthogonal polynomials) are presented and their merits discussed. All the methods presented can be understood on an intuitive level; however, exercises and supplemental materials are provided for those readers desiring a deeper understanding of the techniques. The methods covered in this text have numerous applications in many areas using statistical analysis. Examples are drawn from economics as well as from other disciplines including medicine and engineering.


Refine List
Actions for selected content:
Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Send to Kindle
  • Send to Dropbox
  • Send to Google Drive
  • Send content to

    To send content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about sending content to .

    To send content items to your Kindle, first ensure is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle.

    Note you can select to send to either the or variations. ‘’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

    Find out more about the Kindle Personal Document Service.

    Please be advised that item(s) you selected are not available.
    You are about to send

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.


Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Book summary page views

Total views: 0 *
Loading metrics...

* Views captured on Cambridge Core between #date#. This data will be updated every 24 hours.

Usage data cannot currently be displayed