Skip to content

SCH

Seminar

Limiting theorems for large dimensional sample means, sample covariance matrices and Hotelling's T2 statistics

Pan, G (Eurandom)
Thursday 26 June 2008, 14:00-14:20

Seminar Room 1, Newton Institute

Abstract

It is well known that sample means and sample covariance matrices are independent if the samples are from the Gaussian distribution and are i.i.d.. In this talk, via investigating the random quardratic forms involving sample means and sample covariance matrices, we suggest the conjecture that the sample means and the sample covariance matrices under general distribution functions are asymptotically independent in the large dimensional case when the dimension of the vectors and the sample size both go to infinity with their ratio being a positive constant. As a byproduct, the central limit theorem for the Hotelling $T^2$ statistic under the large dimensional case is established.

Presentation

[pdf ]

Audio

MP3MP3

Video

The video for this talk should appear here if JavaScript is enabled.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.

Back to top ∧