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Article Dans Une Revue Communications in Statistics - Theory and Methods Année : 2007

Estimation and Test for Multi-Dimensional Regression Models

Résumé

This work is concerned with the estimation of multidimensional regression and the asymptotic behaviour of the test involved in selecting models. The main problem with such models is that we need to know the covariance matrix of the noise to get an optimal estimator. We show in this paper that if we choose to minimise the logarithm of the determinant of the empirical error covariance matrix, then we get an asymptotically optimal estimator. Moreover, under suitable assumptions, we show that this cost function leads to a very simple asymptotic law for testing the number of parameters of an identifiable and regular regression model. Numerical experiments confirm the theoretical results.
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Dates et versions

hal-00257516 , version 1 (19-02-2008)

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Citer

Joseph Rynkiewicz. Estimation and Test for Multi-Dimensional Regression Models. Communications in Statistics - Theory and Methods, 2007, 36 (14), pp.2655-2671. ⟨10.1080/03610920701271194⟩. ⟨hal-00257516⟩
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