ReviewMethodsX2026
Recent advancements in integer-valued autoregressive models for count data time series: A comprehensive review.
Review in MethodsX, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Count data time series, characterized by non-negative integer values, frequently arise across diverse domains, including finance, public health, economics, epidemiology, and environmental sciences. Such series often exhibit characteristics such as equidispersion, overdispersion, underdispersion, and zero-inflation/deflation. Failure to appropriately account for these features can result in biased parameter estimates and misleading statistical inference. This review presents a comprehensive overview of recent methodological developments in integer-valued autoregressive (INAR) models, with particular emphasis on thinning operators, estimation methods, and model extensions. A systematic literature search was conducted using electronic databases, including Scopus and Google Scholar, to identify relevant studies published between 2010 and 2024. Recent research has primarily focused on the development of novel thinning operators and flexible innovation distributions aimed at constructing unified modeling frameworks capable of accommodating multiple characteristics of count data simultaneously. This review highlights prevailing research trends, identifies existing methodological gaps, and outlines promising directions for future research in count data time series modeling.
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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.