5 papers
Covariance matrix testing in high dimension using random projections
Deepak Nag Ayyala, Santu Ghosh, Daniel F. Linder
Estimation and hypothesis tests for the covariance matrix in high dimensions is a challenging problem as the traditional multivariate asymptotic theory is no longer valid. When the…
Two-Sample High Dimensional Mean Test Based On Prepivots
Santu Ghosh, Deepak Nag Ayyala, Rafael Hellebuyck
Testing equality of mean vectors is a very commonly used criterion when comparing two multivariate random variables. Traditional tests such as Hotelling's T-squared become either u…
High dimensional statistical inference: theoretical development to data analytics
Deepak Nag Ayyala
This article is due to appear in the Handbook of Statistics, Vol. 43, Elsevier/North-Holland, Amsterdam, edited by Arni S. R. Srinivasa Rao and C. R. Rao. In modern day analytics,…
Note on Mean Vector Testing for High-Dimensional Dependent Observations
Seonghun Cho, Johan Lim, Deepak Nag Ayyala +2
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, t…
Mean vector testing for high dimensional dependent observations
Deepak Nag Ayyala, Junyong Park, Anindya Roy
When testing for the mean vector in a high dimensional setting, it is generally assumed that the observations are independently and identically distributed. However if the data are…