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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.

Suggested Citation

  • Mora, Juan, 2005. "Comparing distribution functions of errors in linear models: A nonparametric approach," Statistics & Probability Letters, Elsevier, vol. 73(4), pages 425-432, July.
  • Handle: RePEc:eee:stapro:v:73:y:2005:i:4:p:425-432
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    References listed on IDEAS

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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;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 361-411, September.

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