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By Arthur Owen


Smooth curves often used to illustrate the relationship between two vari-ables. They are also an important building block in many recent statistical models. A procedure to estimate such a curve is called a smoother. This paper discusses currently available smoothers and introduces the class of maximum likelihood smoothers. A variety of other statistical techniques are shown to be applicable to the problem of smoothing, and some idea of the scope of models that can benefit from the use of smoothing is given

Topics: Transformations, Smoothing, Least Squares, Maximum Likeli- hood, Information, ACE, Projection Pursuit Regression, simultaneous confidence envelopes, bootstrap, non-parametric regression, smoothing splines, M-estimat- ion, Method of Moments
Year: 1984
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