EconStor >
Humboldt-Universität zu Berlin >
CASE - Center for Applied Statistics and Economics, Humboldt-Universität Berlin >
Papers, CASE - Center for Applied Statistics and Economics, HU Berlin >

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10419/22186
  
Title:Smoothing: Local Regression Techniques PDF Logo
Authors:Loader, Catherine
Issue Date:2004
Series/Report no.:Papers / Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE) 2004,12
Abstract:Smoothing methods attempt to find functional relationships between different measurements. As in the standard regression setting, the data is assumed to consist of measurements of a response variable, and one or more predictor variables. Standard regression techniques (Chapter ??) specify a functional form (such as a straight line) to describe the relation between the predictor and response variables. Smoothing methods take a more flexible approach, allowing the data points themselves to determine the form of the fitted curve. This article begins by describing several different approaches to smoothing, including kernel methods, local regression, spline methods and orthogonal series. A general theory of linear smoothing is presented, which allows us to develop methods for statistical inference, model diagnostics and choice of smoothing parameters. The theory is then extended to more general settings, including multivariate smoothing and likelihood models.
Document Type:Working Paper
Appears in Collections:Papers, CASE - Center for Applied Statistics and Economics, HU Berlin

Files in This Item:
File Description SizeFormat
12_cl.pdf211.55 kBAdobe PDF
No. of Downloads: Counter Stats
Download bibliographical data as: BibTeX
Share on:http://hdl.handle.net/10419/22186

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.