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|Title: ||Testing the fit of a parametric function|
|Authors: ||AERTS, Marc|
|Keywords: ||Non and semiparametric methods|
|Issue Date: ||1999|
|Publisher: ||AMER STATISTICAL ASSOC|
|Citation: ||Journal of the American Statistical Association, 94. p. 869-879|
|Abstract: ||General methods for testing the fit of a parametric function are proposed.
The idea underlying each method is to ``accept'' the prescribed parametric
model if and only if it is chosen by a model selection criterion. Several
different selection criteria are considered, including one based on a
modified version of the Akaike information criterion and others based on
various score statistics. The tests have a connection with nonparametric smoothing because they use orthogonal series estimators to detect departures from a parametric model. An important aspect of the tests is that they can be applied in a wide variety of settings, including generalized linear models, spectral analysis, the goodness-of-fit problem, and longitudinal data
analysis. Implementation using standard statistical software is straightforward. Asymptotic distribution theory for several test statistics is described,
and the tests are shown to be consistent against essentially any alternative hypothesis. Simulations and a data example illustrate the usefulness of the tests.|
|ISI #: ||000082756400030|
|Type: ||Journal Contribution|
|Validation: ||ecoom, 2000|
|Appears in Collections: ||Research publications|
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