| Title |
A random forest algorithm to improve the Lee–Carter mortality forecasting: impact on q-forward
|
|---|---|
| Published in |
Soft Computing, October 2019
|
| DOI | 10.1007/s00500-019-04427-z |
| Authors |
Susanna Levantesi, Andrea Nigri |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Italy | 2 | 33% |
| Denmark | 1 | 17% |
| South Africa | 1 | 17% |
| Unknown | 2 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 67% |
| Scientists | 2 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 13 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 2 | 15% |
| Student > Ph. D. Student | 2 | 15% |
| Unspecified | 1 | 8% |
| Professor > Associate Professor | 1 | 8% |
| Student > Doctoral Student | 1 | 8% |
| Other | 1 | 8% |
| Unknown | 5 | 38% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 2 | 15% |
| Mathematics | 1 | 8% |
| Unspecified | 1 | 8% |
| Economics, Econometrics and Finance | 1 | 8% |
| Business, Management and Accounting | 1 | 8% |
| Other | 0 | 0% |
| Unknown | 7 | 54% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 October 2019.
All research outputs
#6,466,849
of 23,839,820 outputs
Outputs from Soft Computing
#64
of 465 outputs
Outputs of similar age
#116,534
of 363,205 outputs
Outputs of similar age from Soft Computing
#2
of 11 outputs
Altmetric has tracked 23,839,820 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 465 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done well, scoring higher than 86% 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 363,205 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 67% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.