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Inference for high‐dimensional linear models with locally stationary error processes

Overview of attention for article published in Journal of Time Series Analysis, April 2023
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

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5 X users

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1 Mendeley
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Title
Inference for high‐dimensional linear models with locally stationary error processes
Published in
Journal of Time Series Analysis, April 2023
DOI 10.1111/jtsa.12686
Authors

Jiaqi Xia, Yu Chen, Xiao Guo

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 1 100%
Readers by discipline Count As %
Economics, Econometrics and Finance 1 100%
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
#13,983,872
of 24,495,755 outputs
Outputs from Journal of Time Series Analysis
#87
of 413 outputs
Outputs of similar age
#160,752
of 404,295 outputs
Outputs of similar age from Journal of Time Series Analysis
#2
of 15 outputs
Altmetric has tracked 24,495,755 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 413 research outputs from this source. They receive a mean Attention Score of 1.8. This one has done well, scoring higher than 78% 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 404,295 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 59% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.