Abstract: Introduction to Nonparametric Regression

Education and Training: Introduction to Nonparametric Regression
Time and Location	Introduction to Nonparametric Regression Will Guthrie NIST Statistical Engineering Division Friday September 8, 2004 9:30 am - 12:00 noon Adminstration Building, Lecture Room D NIST Gaithersburg, MD
Abstract	Non-parametric regression is a type of regression analysis in which the functional form of the relationship between the response variable and the associated predictor variables does not to be specified in order to fit a model to a set of data. There are many different methods for non-parametric regression, which often use different types of simple, local models in different sections of the data to build up an overall model of the data. This makes non-parametric regression a good competitor to non-linear regression for modeling situations in which a theoretical model is not known, or is difficult to fit. Non-parametric regression models can generally be used for the same types of applications, estimation, prediction, calibration, and optimization, that traditional regression models are used for. This course will introduce some popular non-parametric regression methods and compare and contrast how they work and can be used with traditional linear and non-linear regression models. Concepts will be illustrated with many examples from NIST work.
Comments on Course	CLASS SIZE IS LIMITED TO 40. There is a $75 registration fee for this class. A set of notes will be provided for the class.
Further Information	For further information, contact Sandra Heydemann (ext. 3032; sandra.heydemann@nist.gov) Will Guthrie (ext. 2854; william.guthrie@nist.gov) or register online at http://www-i.nist.gov/cgi-bin/training.cgi
Date created: 10/16/2002 Last updated: 08/30/2004 Please email comments on this WWW page to sedwww@nist.gov.

Education and Training: Introduction to Nonparametric Regression