Note on Mean Vector Testing for High-Dimensional Dependent Observations

by   Seonghun Cho, et al.
Augusta University

For the mean vector test in high dimension, Ayyala et al.(2017,153:136-155) proposed new test statistics when the observational vectors are M dependent. Under certain conditions, the test statistics for one-same and two-sample cases were shown to be asymptotically normal. While the test statistics and the asymptotic results are valid, some parts of the proof of asymptotic normality need to be corrected. In this work, we provide corrections to the proofs of their main theorems. We also note a few minor discrepancies in calculations in the publication.


page 1

page 2

page 3

page 4


A Pairwise Hotelling Method for Testing High-Dimensional Mean Vectors

For high-dimensional small sample size data, Hotelling's T2 test is not ...

Adaptive Testing for High-dimensional Data

In this article, we propose a class of L_q-norm based U-statistics for a...

High-dimensional CLT for Sums of Non-degenerate Random Vectors: n^-1/2-rate

In this note, we provide a Berry–Esseen bounds for rectangles in high-di...

Asymptotically optimal test for dependent multiple testing set up

In this paper we explore the behaviour of dependent test statistics for ...

Corrected Kriging update formulae for batch-sequential data assimilation

Recently, a lot of effort has been paid to the efficient computation of ...

Asymptotic bayes optimality under sparsity for equicorrelated multivariate normal test statistics

Here we address dependence among the test statistics in connection with ...

Hypothesis Testing of One-Sample Mean Vector in Distributed Frameworks

Distributed frameworks are widely used to handle massive data, where sam...

Please sign up or login with your details

Forgot password? Click here to reset