Abstract
In this chapter we consider testing the fit of parametric models of a more general nature than the constant mean model of Chapter 7. We begin with the case of a linear model, i.e., the case where r is hypothesized to be a linear combination of known functions. The fit of such models can be tested by 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 if least squares is used to estimate model parameters.
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© 1997 Springer Science+Business Media New York
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Hart, J.D. (1997). Data-Driven Lack-of-Fit Tests for General Parametric Models. In: Nonparametric Smoothing and Lack-of-Fit Tests. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-2722-7_8
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DOI: https://doi.org/10.1007/978-1-4757-2722-7_8
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4757-2724-1
Online ISBN: 978-1-4757-2722-7
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