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Comparing distribution functions of errors in linear models: A nonparametric approach

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  • Mora, Juan

Abstract

We describe how to test whether the distribution functions of errors from two linear regression models are the same, with statistics based on empirical distribution functions constructed with residuals. A smooth bootstrap method is used to approximate critical values. Simulations show that the procedure works well in practice.

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Bibliographic Info

Article provided by Elsevier in its journal Statistics & Probability Letters.

Volume (Year): 73 (2005)
Issue (Month): 4 (July)
Pages: 425-432

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Handle: RePEc:eee:stapro:v:73:y:2005:i:4:p:425-432

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Related research

Keywords: Two-sample problem Residual-based empirical process Smooth bootstrap;

References

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  1. Koul, H. L. & Lahiri, S. N., 1994. "On Bootstrapping M-Estimated Residual Processes in Multiple Linear-Regression Models," Journal of Multivariate Analysis, Elsevier, vol. 49(2), pages 255-265, May.
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Cited by:
  1. Wenceslao González-Manteiga & Rosa Crujeiras, 2013. "An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 22(3), pages 361-411, September.

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