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Efficient and fast spline-backfitted kernel smoothing of additive models

Overview of attention for article published in Annals of the Institute of Statistical Mathematics, October 2007
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (65th percentile)

Mentioned by

policy
1 policy source

Citations

dimensions_citation
22 Dimensions

Readers on

mendeley
9 Mendeley
Title
Efficient and fast spline-backfitted kernel smoothing of additive models
Published in
Annals of the Institute of Statistical Mathematics, October 2007
DOI 10.1007/s10463-007-0157-x
Authors

Jing Wang, Lijian Yang

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 2 22%
Researcher 2 22%
Student > Ph. D. Student 2 22%
Student > Postgraduate 1 11%
Other 1 11%
Other 1 11%
Readers by discipline Count As %
Engineering 2 22%
Mathematics 2 22%
Economics, Econometrics and Finance 2 22%
Computer Science 1 11%
Earth and Planetary Sciences 1 11%
Other 1 11%

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 02 July 2015.
All research outputs
#3,547,024
of 12,341,272 outputs
Outputs from Annals of the Institute of Statistical Mathematics
#14
of 57 outputs
Outputs of similar age
#87,637
of 270,067 outputs
Outputs of similar age from Annals of the Institute of Statistical Mathematics
#1
of 1 outputs
Altmetric has tracked 12,341,272 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 57 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
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 270,067 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 65% of its contemporaries.
We're also able to compare this research output to 1 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