Computational and Mathematical Methods最新文献_第2页

On Efficient Iterative Algorithms and Conditioning for the Nonlinear Sylvester Equation 非线性Sylvester方程的有效迭代算法与条件

IF 1.5

Computational and Mathematical Methods Pub Date : 2026-06-17 DOI: 10.1155/cmm4/9773355

Constantino Mahinya, Chacha Stephen Chacha

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引用次数: 0

A Fractional-Order Discrete-Time FitzHugh–Nagumo Model With Multiple Time Delays 具有多时滞的分数阶离散FitzHugh-Nagumo模型

IF 1.5

Computational and Mathematical Methods Pub Date : 2026-06-15 DOI: 10.1155/cmm4/8170657

Iqbal M. Batiha, Shaher Momani, Lina Hushki, Lina H. Calucag, Anjan Biswas

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引用次数: 0

A Fractional-Order Discrete-Time FitzHugh–Nagumo Model With Multiple Time Delays 具有多时滞的分数阶离散FitzHugh-Nagumo模型

IF 1.5

Computational and Mathematical Methods Pub Date : 2026-06-15 DOI: 10.1155/cmm4/8170657

Iqbal M. Batiha, Shaher Momani, Lina Hushki, Lina H. Calucag, Anjan Biswas

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引用次数: 0

Discretized Trigonometric Burr–Hatke Distributions: Properties, INAR(1) Process, and Population Size Estimation 离散三角Burr-Hatke分布：性质、INAR(1)过程和人口规模估计

IF 1.2

Computational and Mathematical Methods Pub Date : 2026-06-08 DOI: 10.1155/cmm4/2164001

Caleb Nurideen Nambyn, Dioggban Jakperik, Suleman Nasiru, Christophe Chesneau

{"title":"Discretized Trigonometric Burr–Hatke Distributions: Properties, INAR(1) Process, and Population Size Estimation","authors":"Caleb Nurideen Nambyn, Dioggban Jakperik, Suleman Nasiru, Christophe Chesneau","doi":"10.1155/cmm4/2164001","DOIUrl":"https://doi.org/10.1155/cmm4/2164001","url":null,"abstract":"This study presents a novel suite of discrete trigonometric Burr–Hatke distributions, encompassing the discrete tangent Burr–Hatke (DTBH), discrete sine Burr–Hatke (DSBH), discrete cosine Burr–Hatke (DCBH), and discrete type II tangent Burr–Hatke (DTIITBH) distributions to model count data with overdispersion and right-skewed characteristics commonly observed in count data. Empirical analyses using three real-world datasets show that the DTBH and DCBH models, respectively, outperform established alternatives according to model evaluation metrics. This research further proposes their integration into time series and zero-truncated frameworks. A first-order integer-valued autoregressive process (INAR(1)) utilizing negative binomial thinning and DCBH innovations is formulated, termed the NBINAR (1)-DCBHD model. The proposed NBINAR (1)-DCBHD model delivers superior fit and predictive accuracy, validated by lowest AIC, BIC, RMSE, and satisfactory diagnostic tests. Additionally, to address zero-truncated count scenarios, the zero-truncated discrete cosine Burr–Hatke distribution (ZTDCBHD) is proposed and shown to outperform other existing distributions. Estimating population size with the Horvitz–Thompson estimator under the ZTDCBHD yields improved estimates for hidden populations across two different datasets and further validated using a dataset with known population size. The study establishes that the proposed discrete trigonometric Burr–Hatke models present a flexible option for modeling count data in time series and population estimation settings.","PeriodicalId":100308,"journal":{"name":"Computational and Mathematical Methods","volume":"2026 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/cmm4/2164001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discretized Trigonometric Burr–Hatke Distributions: Properties, INAR(1) Process, and Population Size Estimation 离散三角Burr-Hatke分布：性质、INAR(1)过程和人口规模估计

IF 1.2

Computational and Mathematical Methods Pub Date : 2026-06-08 DOI: 10.1155/cmm4/2164001

Caleb Nurideen Nambyn, Dioggban Jakperik, Suleman Nasiru, Christophe Chesneau

{"title":"Discretized Trigonometric Burr–Hatke Distributions: Properties, INAR(1) Process, and Population Size Estimation","authors":"Caleb Nurideen Nambyn, Dioggban Jakperik, Suleman Nasiru, Christophe Chesneau","doi":"10.1155/cmm4/2164001","DOIUrl":"https://doi.org/10.1155/cmm4/2164001","url":null,"abstract":"This study presents a novel suite of discrete trigonometric Burr–Hatke distributions, encompassing the discrete tangent Burr–Hatke (DTBH), discrete sine Burr–Hatke (DSBH), discrete cosine Burr–Hatke (DCBH), and discrete type II tangent Burr–Hatke (DTIITBH) distributions to model count data with overdispersion and right-skewed characteristics commonly observed in count data. Empirical analyses using three real-world datasets show that the DTBH and DCBH models, respectively, outperform established alternatives according to model evaluation metrics. This research further proposes their integration into time series and zero-truncated frameworks. A first-order integer-valued autoregressive process (INAR(1)) utilizing negative binomial thinning and DCBH innovations is formulated, termed the NBINAR (1)-DCBHD model. The proposed NBINAR (1)-DCBHD model delivers superior fit and predictive accuracy, validated by lowest AIC, BIC, RMSE, and satisfactory diagnostic tests. Additionally, to address zero-truncated count scenarios, the zero-truncated discrete cosine Burr–Hatke distribution (ZTDCBHD) is proposed and shown to outperform other existing distributions. Estimating population size with the Horvitz–Thompson estimator under the ZTDCBHD yields improved estimates for hidden populations across two different datasets and further validated using a dataset with known population size. The study establishes that the proposed discrete trigonometric Burr–Hatke models present a flexible option for modeling count data in time series and population estimation settings.","PeriodicalId":100308,"journal":{"name":"Computational and Mathematical Methods","volume":"2026 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/cmm4/2164001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 1.2

Computational and Mathematical Methods Pub Date : 2026-05-20