PMID- 26924939
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240324
IS  - 1369-7412 (Print)
IS  - 1369-7412 (Linking)
VI  - 78
IP  - 2
DP  - 2016 Mar 1
TI  - Joint Estimation of Multiple Graphical Models from High Dimensional Time Series.
PG  - 487-504
AB  - In this manuscript we consider the problem of jointly estimating multiple 
      graphical models in high dimensions. We assume that the data are collected from n 
      subjects, each of which consists of T possibly dependent observations. The 
      graphical models of subjects vary, but are assumed to change smoothly 
      corresponding to a measure of closeness between subjects. We propose a kernel 
      based method for jointly estimating all graphical models. Theoretically, under a 
      double asymptotic framework, where both (T, n) and the dimension d can increase, 
      we provide the explicit rate of convergence in parameter estimation. It 
      characterizes the strength one can borrow across different individuals and the 
      impact of data dependence on parameter estimation. Empirically, experiments on 
      both synthetic and real resting state functional magnetic resonance imaging 
      (rs-fMRI) data illustrate the effectiveness of the proposed method.
FAU - Qiu, Huitong
AU  - Qiu H
AD  - Johns Hopkins University, Baltimore, USA.
FAU - Han, Fang
AU  - Han F
AD  - Johns Hopkins University, Baltimore, USA.
FAU - Liu, Han
AU  - Liu H
AD  - Princeton University, Princeton, USA.
FAU - Caffo, Brian
AU  - Caffo B
AD  - Johns Hopkins University, Baltimore, USA.
LA  - eng
GR  - R01 HL123407/HL/NHLBI NIH HHS/United States
GR  - R01 EB012547/EB/NIBIB NIH HHS/United States
GR  - R01 MH102339/MH/NIMH NIH HHS/United States
GR  - P41 EB015909/EB/NIBIB NIH HHS/United States
GR  - R01 NS060910/NS/NINDS NIH HHS/United States
PT  - Journal Article
DEP - 20150706
PL  - England
TA  - J R Stat Soc Series B Stat Methodol
JT  - Journal of the Royal Statistical Society. Series B, Statistical methodology
JID - 100890344
PMC - PMC4767508
MID - NIHMS708139
OTO - NOTNLM
OT  - Conditional independence
OT  - Graphical model
OT  - High dimensional data
OT  - Rate of convergence
OT  - Time series
EDAT- 2016/03/01 06:00
MHDA- 2016/03/01 06:01
PMCR- 2016/06/01
CRDT- 2016/03/01 06:00
PHST- 2016/03/01 06:00 [entrez]
PHST- 2016/03/01 06:00 [pubmed]
PHST- 2016/03/01 06:01 [medline]
PHST- 2016/06/01 00:00 [pmc-release]
AID - 10.1111/rssb.12123 [doi]
PST - ppublish
SO  - J R Stat Soc Series B Stat Methodol. 2016 Mar 1;78(2):487-504. doi: 
      10.1111/rssb.12123. Epub 2015 Jul 6.