↓ Skip to main content

Central limit theorems for high dimensional dependent data

Overview of attention for article published in Bernoulli, February 2024
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (63rd percentile)

Mentioned by

twitter
4 X users

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
8 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Central limit theorems for high dimensional dependent data
Published in
Bernoulli, February 2024
DOI 10.3150/23-bej1614
Authors

Jinyuan Chang, Xiaohui Chen, Mingcong Wu

Timeline

Login to access the full chart related to this output.

If you don’t have an account, click here to discover Explorer

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 25%
Student > Postgraduate 1 13%
Student > Master 1 13%
Unknown 4 50%
Readers by discipline Count As %
Mathematics 2 25%
Computer Science 1 13%
Economics, Econometrics and Finance 1 13%
Unknown 4 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 March 2023.
All research outputs
#14,611,205
of 25,387,668 outputs
Outputs from Bernoulli
#94
of 449 outputs
Outputs of similar age
#88,151
of 250,088 outputs
Outputs of similar age from Bernoulli
#1
of 3 outputs
Altmetric has tracked 25,387,668 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 449 research outputs from this source. They receive a mean Attention Score of 1.8. This one has done well, scoring higher than 77% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 250,088 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them