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:2018:10.1214/17-AOS1587,
author = {Battey, HS and Fan, J and Liu, H and Lu, J and Zhu, Z},
doi = {10.1214/17-AOS1587},
journal = {Annals of Statistics},
pages = {1352--1382},
title = {Distributed testing and estimation in sparse high dimensional models},
url = {http://dx.doi.org/10.1214/17-AOS1587},
volume = {46},
year = {2018}
}
TY - JOUR
AB - This paper studies hypothesis testing and parameter estimation in the context of the divide-and-conquer algorithm. In a unified likelihood-based framework, we propose new test statistics and point estimators obtained by aggregating various statistics from k subsamples of size n/k, where n is the sample size. In both low dimensional and sparse high dimensional settings, we address the important question of how large k can be, as n grows large, such that the loss of efficiency due to the divide-and-conquer algorithm is negligible. In other words, the resulting estimators have the same inferential efficiencies and estimation rates as an oracle with access to the full sample. Thorough numerical results are provided to back up the theory.
AU - Battey,HS
AU - Fan,J
AU - Liu,H
AU - Lu,J
AU - Zhu,Z
DO - 10.1214/17-AOS1587
EP - 1382
PY - 2018///
SN - 0090-5364
SP - 1352
TI - Distributed testing and estimation in sparse high dimensional models
T2 - Annals of Statistics
UR - http://dx.doi.org/10.1214/17-AOS1587
UR - http://hdl.handle.net/10044/1/48600
VL - 46
ER -