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{Battey:2019:biomet/asz014,
author = {Battey, HS},
doi = {biomet/asz014},
journal = {Biometrika},
pages = {605--617},
title = {On sparsity scales and covariance matrix transformations},
url = {http://dx.doi.org/10.1093/biomet/asz014},
volume = {106},
year = {2019}
}
TY - JOUR
AB - We develop a theory of covariance and concentration matrix estimation on any given or estimated sparsity scale when the matrix dimension is larger than the sample size. Nonstandard sparsity scales are justified when such matrices are nuisance parameters, distinct from interest parameters, which should always have a direct subject-matter interpretation. The matrix logarithmic and inverse scales are studied as special cases, with the corollary that a constrained optimization-based approach is unnecessary for estimating a sparse concentration matrix. It is shown through simulations that for large unstructured covariance matrices, there can be appreciable advantages to estimating a sparse approximation to the log-transformed covariance matrix and converting the conclusions back to the scale of interest.
AU - Battey,HS
DO - biomet/asz014
EP - 617
PY - 2019///
SN - 0006-3444
SP - 605
TI - On sparsity scales and covariance matrix transformations
T2 - Biometrika
UR - http://dx.doi.org/10.1093/biomet/asz014
UR - http://hdl.handle.net/10044/1/66425
VL - 106
ER -