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An integer-valued moving average model is created in this study to account for dependence and overdispersion in count time series. Logarithmic distribution innovations, which offer a versatile framework for managing severely skewed and overdispersed data, are used in the Logarithmic DCINMA(q) model. The model's theoretical underpinnings are established by deriving important statistical features, such as the mean, variance, and autocovariance structure. The Method of Moments (MoM), which makes use of closed-form expressions of the process moments, is used to estimate parameters. To assess the estimator's finite-sample performance under various parameter configurations and sample sizes, a simulation analysis is carried out. The outcomes show the usefulness of the suggested model for examining actual count procedures in the real world by confirming consistency and reducing bias with larger samples. The National Bureau of Statistics' monthly recorded crime data for Lagos State, Nigeria (2008–2021) is used to further demonstrate the model's applicability.
Keywords:
Logarithmic distribution, moving average model, overdispersion, integer-valued time series, and the method of moments.
Cite Article:
"LOGARITHMIC DEPENDENT COUNT INTEGER-VALUED MOVING AVERAGE MODEL (LOGARITHMIC DCINMA(Q))", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 2, page no.a383-a391, February-2026, Available :http://www.ijrti.org/papers/IJRTI2602051.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator