Estimation and Test for Multidimensional Regression Models

Details Estimation and Test for Multidimensional Regression Models Estimation and Test for Multidimensional Regression Models

2008-02-19

arxiv.org/abs/0802.2676v1

Communication in Statistics - Theory and Methods 36, 14 (2007) 2655-2671

Mathematics

Statistics Theory

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Estimation and Test for Multidimensional Regression Models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Estimation and Test for Multidimensional Regression Models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Estimation and Test for Multidimensional Regression Models will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-338847