Testing goodness of fit for the distribution of errors in multivariate linear models
Journal of multivariate analysis, 2005•Elsevier
In this paper, to test goodness of fit to any fixed distribution of errors in multivariate linear
models, we consider a weighted integral of the squared modulus of the difference between
the empirical characteristic function of the residuals and the characteristic function under the
null hypothesis. We study the limiting behaviour of this test statistic under the null hypothesis
and under alternatives. In the asymptotics, the rank of the design matrix is allowed to grow
with the sample size.
models, we consider a weighted integral of the squared modulus of the difference between
the empirical characteristic function of the residuals and the characteristic function under the
null hypothesis. We study the limiting behaviour of this test statistic under the null hypothesis
and under alternatives. In the asymptotics, the rank of the design matrix is allowed to grow
with the sample size.
In this paper, to test goodness of fit to any fixed distribution of errors in multivariate linear models, we consider a weighted integral of the squared modulus of the difference between the empirical characteristic function of the residuals and the characteristic function under the null hypothesis. We study the limiting behaviour of this test statistic under the null hypothesis and under alternatives. In the asymptotics, the rank of the design matrix is allowed to grow with the sample size.
Elsevier
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