In this section
Most of the members of this group are from the Statistics Section and Biomaths research group of the Department of Mathematics. Below you can find a list of research areas that members of this group are currently working on and/or would like to work on by applying their developed mathematical and statistical methods.
@article{Avella:2018:biomet/asy011,
author = {Avella, M and Battey, HS and Fan, J and Li, Q},
doi = {biomet/asy011},
journal = {Biometrika},
pages = {271--284},
title = {Robust estimation of high-dimensional covariance and precision matrices},
url = {http://dx.doi.org/10.1093/biomet/asy011},
volume = {105},
year = {2018}
}
TY - JOUR
AB - High-dimensional data are often most plausibly generated from distributions with complex structure and leptokurtosis in some or all components. Covariance and precision matrices provide a useful summary of such structure, yet the performance of popular matrix estimators typically hinges upon a sub-Gaussianity assumption. This paper presents robust matrix estimators whose performance is guaranteed for a much richer class of distributions. The proposed estimators, under a bounded fourth moment assumption, achieve the same minimax convergence rates as do existing methods under a sub-Gaussianity assumption. Consistency of the proposed estimators is also established under the weak assumption of bounded2+moments for∈(0,2). The associated convergence rates depend on.
AU - Avella,M
AU - Battey,HS
AU - Fan,J
AU - Li,Q
DO - biomet/asy011
EP - 284
PY - 2018///
SN - 0006-3444
SP - 271
TI - Robust estimation of high-dimensional covariance and precision matrices
T2 - Biometrika
UR - http://dx.doi.org/10.1093/biomet/asy011
VL - 105
ER -
If you are interested in meeting with members of the group please contact Marina Evangelou