Data-Driven Lack-of-Fit Tests for General Parametric Models

  • Jeffrey D. Hart
Part of the Springer Series in Statistics book series (SSS)


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.


Fourier Coefficient Null Distribution Multivariate Normal Distribution Local Polynomial Pivotal Quantity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • Jeffrey D. Hart
    • 1
  1. 1.Department of StatisticsTexas A&M UniversityCollege StationUSA

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