| Title |
Using visual statistical inference to better understand random class separations in high dimension, low sample size data
|
|---|---|
| Published in |
Computational Statistics, November 2014
|
| DOI | 10.1007/s00180-014-0534-x |
| Authors |
Niladri Roy Chowdhury, Dianne Cook, Heike Hofmann, Mahbubul Majumder, Eun-Kyung Lee, Amy L. Toth |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 2 | 25% |
| United Kingdom | 1 | 13% |
| Japan | 1 | 13% |
| United States | 1 | 13% |
| Tunisia | 1 | 13% |
| Switzerland | 1 | 13% |
| Unknown | 1 | 13% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 50% |
| Members of the public | 3 | 38% |
| Practitioners (doctors, other healthcare professionals) | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 8% |
| Russia | 1 | 4% |
| Denmark | 1 | 4% |
| Taiwan | 1 | 4% |
| Unknown | 21 | 81% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 31% |
| Researcher | 4 | 15% |
| Student > Doctoral Student | 3 | 12% |
| Other | 2 | 8% |
| Student > Master | 2 | 8% |
| Other | 4 | 15% |
| Unknown | 3 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Mathematics | 6 | 23% |
| Computer Science | 5 | 19% |
| Biochemistry, Genetics and Molecular Biology | 3 | 12% |
| Social Sciences | 2 | 8% |
| Psychology | 2 | 8% |
| Other | 5 | 19% |
| Unknown | 3 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 04 May 2021.
All research outputs
#2,244,086
of 22,836,570 outputs
Outputs from Computational Statistics
#6
of 165 outputs
Outputs of similar age
#27,848
of 261,554 outputs
Outputs of similar age from Computational Statistics
#1
of 4 outputs
Altmetric has tracked 22,836,570 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 165 research outputs from this source. They receive a mean Attention Score of 3.3. This one has done particularly well, scoring higher than 96% 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 261,554 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 4 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