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Clustering Vector Autoregressive Models: Capturing Qualitative Differences in Within-Person Dynamics

Overview of attention for article published in Frontiers in Psychology, October 2016
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Title
Clustering Vector Autoregressive Models: Capturing Qualitative Differences in Within-Person Dynamics
Published in
Frontiers in Psychology, October 2016
DOI 10.3389/fpsyg.2016.01540
Pubmed ID
Authors

Kirsten Bulteel, Francis Tuerlinckx, Annette Brose, Eva Ceulemans

Abstract

In psychology, studying multivariate dynamical processes within a person is gaining ground. An increasingly often used method is vector autoregressive (VAR) modeling, in which each variable is regressed on all variables (including itself) at the previous time points. This approach reveals the temporal dynamics of a system of related variables across time. A follow-up question is how to analyze data of multiple persons in order to grasp similarities and individual differences in within-person dynamics. We focus on the case where these differences are qualitative in nature, implying that subgroups of persons can be identified. We present a method that clusters persons according to their VAR regression weights, and simultaneously fits a shared VAR model to all persons within a cluster. The performance of the algorithm is evaluated in a simulation study. Moreover, the method is illustrated by applying it to multivariate time series data on depression-related symptoms of young women.

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The data shown below were collected from the profiles of 2 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 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Unknown 63 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 17%
Student > Ph. D. Student 9 14%
Student > Master 8 13%
Student > Doctoral Student 7 11%
Student > Bachelor 4 6%
Other 9 14%
Unknown 16 25%
Readers by discipline Count As %
Psychology 21 33%
Mathematics 5 8%
Computer Science 3 5%
Business, Management and Accounting 3 5%
Neuroscience 3 5%
Other 8 13%
Unknown 21 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 20 May 2020.
All research outputs
#14,864,294
of 22,893,031 outputs
Outputs from Frontiers in Psychology
#16,167
of 30,015 outputs
Outputs of similar age
#192,464
of 320,336 outputs
Outputs of similar age from Frontiers in Psychology
#316
of 458 outputs
Altmetric has tracked 22,893,031 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 30,015 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.5. This one is in the 38th percentile – i.e., 38% 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 320,336 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 458 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.