Evidence map›Paper›PMID 39071254›Full record

ArticleJournal of applied statistics2024

GARTFIMA process and its empirical spectral density based estimation.

Niharika Bhootna, Arun Kumar

Abstract read
In one paragraph

Article in Journal of applied statistics, 2024. 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

2 authors.

Niharika BhootnaDepartment of Mathematics, Indian Institute of Technology Ropar, Rupnagar, Punjab, India.
Arun KumarDepartment of Mathematics, Indian Institute of Technology Ropar, Rupnagar, Punjab, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this article, we introduce a Gegenbauer autoregressive tempered fractionally integrated moving average process. We work on the spectral density and autocovariance function for the introduced process. The parameter estimation is done using the empirical spectral density with the help of the nonlinear least square technique and the Whittle likelihood estimation technique. The performance of the proposed estimation techniques is assessed on simulated data. Further, the introduced process is shown to better model the real-world data in comparison to other time series models.

Indexed as

62M10Fractional ARIMA processesGegenbauer processesparameter estimationspectral densitytempered fractional ARIMA processes

Identifiers

PMID39071254
PMCPMC11271098

What OpenQuestion holds

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Registered trials

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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.