Titlebook: Nonparametric Smoothing and Lack-of-Fit Tests; Jeffrey D. Hart Book 1997 Springer Science+Business Media New York 1997 Calc.Mathematica.Sc

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Introduction, are many, and include probability density estimation, time series spectrum estimation, and estimation of regression functions. The present treatment will deal primarily with regression, but many of the ideas and methods to be discussed have applications in other areas as well.

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Testing for Association via Automated Order Selection,es use of an orthogonal series representation for r. In principle any series representation could be used, but for now we consider only trigonometric series. This is done for the sake of clarity and to make the ideas less abstract. Section 7.8 discusses the use of other types of orthogonal series.

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Data-Driven Lack-of-Fit Tests for General Parametric Models, applying the methods of Chapter 7 to residuals. It will be argued that test statistics generally have the same distributions they had in Chapter 7 . least squares is used to estimate model parameters.

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Some Basic Ideas of Smoothing,ey (1981), Devroye and Györfi (1985), Silverman (1986), Eubank (1988), Härdle (1990), Wahba (1990), Scott (1992), Tarter and Lock (1993), Green and Silverman (1994), Wand and Jones (1995) and Fan and Gijbels (1996).