Evidence map›Paper›PMID 36530776›Full record

ArticleJournal of applied statistics2023

Elastic statistical analysis of interval-valued time series.

Honggang Zhang, Jingyong Su, Linlin Tang, Anuj Srivastava

Abstract read
In one paragraph

Article in Journal of applied statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Honggang ZhangSchool of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, People's Republic of China.
Jingyong SuSchool of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, People's Republic of China.
Linlin TangSchool of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, People's Republic of China.
Anuj SrivastavaDepartment of Statistics, Florida State University, Tallahassee, Florida, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We investigate the problem of statistical analysis of interval-valued time series data - two nonintersecting real-valued functions, representing lower and upper limits, over a period of time. Specifically, we pay attention to the two concepts of phase (or horizontal) variability and amplitude (or vertical) variability, and propose a phase-amplitude separation method. We view interval-valued time series as elements of a function (Hilbert) space and impose a Riemannian structure on it. We separate phase and amplitude variability in observed interval functions using a metric-based alignment solution. The key idea is to map an interval to a point in

Indexed as

elastic shape analysisinterval-valued time seriesTime warping

Identifiers

PMID36530776
PMCPMC9754051

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.