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Journal of Statistical Computation and Simulation Volume 88, 2018 - Issue 3
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Original Articles

Threshold autoregression analysis for finite-range time series of counts with an application on measles data

Kai YangSchool of Basic Science, Changchun University of Technology, Changchun, Jilin, People's Republic of ChinaCorrespondence[email protected]
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,
Dehui WangSchool of Mathematics, Jilin University, Changchun, Jilin, People's Republic of ChinaView further author information
&
Han LiSchool of Mathematics, Jilin University, Changchun, Jilin, People's Republic of ChinaView further author information
Pages 597-614 | Received 16 Aug 2017, Accepted 30 Oct 2017, Published online: 13 Nov 2017

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Han Yan, Dehui Wang & Cong Li. (2024) A study for the NMBAR(1) processes. Communications in Statistics - Simulation and Computation 53:3, pages 1308-1329.
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Han Li, Xiaoman Chen & Xiaogang Dong. (2024) Self-exciting threshold -valued autoregressive processes for non-stationary time series of counts. Statistics 58:2, pages 316-335.
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Han Li, Haoyu Wang, Kai Yang, Jie Sun & Yan Liu. (2023) A nonparametric Bayesian analysis for meningococcal disease counts based on integer-valued threshold time series models. Communications in Statistics - Theory and Methods 52:22, pages 8223-8240.
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Tengyue Ma & Dehui Wang. (2023) First-order random coefficient binomial AR process with dependent counting series. Communications in Statistics - Simulation and Computation 0:0, pages 1-20.
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Han Yan, Dehui Wang & Zheqi Wang. (2023) First-order binomial autoregressive processes with Markov-switching coefficients. Journal of Statistical Computation and Simulation 93:9, pages 1378-1402.
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Mohamed Bentarzi & Mohamed Sadoun. (2023) Efficient estimation in semiparametric self-exciting threshold INAR processes. Communications in Statistics - Simulation and Computation 52:6, pages 2592-2614.
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Jie Zhang, Siyu Shao, Kai Yang & Xiaogang Dong. (2023) A statistical study for some classes of first-order mixed generalized binomial autoregressive models. Communications in Statistics - Theory and Methods 0:0, pages 1-19.
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Chenhui Zhang, Dehui Wang, Kai Yang, Han Li & Xiaohong Wang. (2022) Generalized Poisson integer-valued autoregressive processes with structural changes. Journal of Applied Statistics 49:11, pages 2717-2739.
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Simon Nik & Christian H. Weiß. (2021) Smooth-transition autoregressive models for time series of bounded counts. Stochastic Models 37:4, pages 568-588.
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Xiaohong Wang, Dehui Wang, Kai Yang & Da Xu. (2021) Estimation and testing for the integer-valued threshold autoregressive models based on negative binomial thinning. Communications in Statistics - Simulation and Computation 50:6, pages 1622-1644.
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Yao Kang, Dehui Wang & Kai Yang. (2020) Extended binomial AR(1) processes with generalized binomial thinning operator. Communications in Statistics - Theory and Methods 49:14, pages 3498-3520.
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Jie Zhang, Dehui Wang & Kai Yang. (2020) A study of RCINAR(1) process with generalized negative binomial marginals. Communications in Statistics - Simulation and Computation 49:6, pages 1487-1510.
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Cong Li, Dehui Wang & Jianguo Sun. (2019) Control charts based on dependent count data with deflation or inflation of zeros. Journal of Statistical Computation and Simulation 89:17, pages 3273-3289.
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Articles from other publishers (16)

Kai Yang, Yiwei Zhao, Han Li & Dehui Wang. (2023) On bivariate threshold Poisson integer-valued autoregressive processes. Metrika 86:8, pages 931-963.
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Han Li, Zijian Liu, Kai Yang, Xiaogang Dong & Wenshan Wang. (2023) A pth-order random coefficients mixed binomial autoregressive process with explanatory variables. Computational Statistics.
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Kai Yang, Xiuyue Zhao, Xiaogang Dong & Christian H. Weiß. (2023) Self-exciting hysteretic binomial autoregressive processes. Statistical Papers.
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Jie Zhang, Jiacong Wang, Zhiyan Tai & Xiaogang Dong. (2022) A study of binomial AR(1) process with an alternative generalized binomial thinning operator. Journal of the Korean Statistical Society 52:1, pages 110-129.
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Yao Kang, Shuhui Wang, Dehui Wang & Fukang Zhu. (2022) Analysis of zero-and-one inflated bounded count time series with applications to climate and crime data. TEST 32:1, pages 34-73.
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Rui Zhang, Dehui Wang & Cong Li. (2022) Flexible binomial AR(1) processes using copulas. Journal of Statistical Planning and Inference 219, pages 306-332.
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Kai Yang, Xinyang Yu, Qingqing Zhang & Xiaogang Dong. (2022) On MCMC sampling in self-exciting integer-valued threshold time series models. Computational Statistics & Data Analysis 169, pages 107410.
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Kai Yang, Xue Ding & Xiaohui Yuan. (2021) Bayesian empirical likelihood inference and order shrinkage for autoregressive models. Statistical Papers 63:1, pages 97-121.
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Kai Yang, Han Li, Dehui Wang & Chenhui Zhang. (2020) Random coefficients integer-valued threshold autoregressive processes driven by logistic regression. AStA Advances in Statistical Analysis 105:4, pages 533-557.
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Jie Zhang, Dehui Wang, Kai Yang & Xiaogang Dong. (2021) First-Order Random Coefficient Multinomial Autoregressive Model for Finite-Range Time Series of Counts. Symmetry 13:12, pages 2271.
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De-hui Wang, Shuai Cui, Jian-hua Cheng & Shu-hui Wang. (2021) Statistical Inference for the Covariates-driven Binomial AR(1) Process. Acta Mathematicae Applicatae Sinica, English Series 37:4, pages 758-772.
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Yao Kang, Dehui Wang & Kai Yang. (2019) A new INAR(1) process with bounded support for counts showing equidispersion, underdispersion and overdispersion. Statistical Papers 62:2, pages 745-767.
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Huaping Chen, Qi Li & Fukang Zhu. (2020) Two classes of dynamic binomial integer-valued ARCH models. Brazilian Journal of Probability and Statistics 34:4.
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Jie Zhang, Dehui Wang, Kai Yang & Yanju Xu. (2020) A multinomial autoregressive model for finite-range time series of counts. Journal of Statistical Planning and Inference 207, pages 320-343.
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Kai Yang, Yao Kang, Dehui Wang, Han Li & Yajing Diao. (2019) Modeling overdispersed or underdispersed count data with generalized Poisson integer-valued autoregressive processes. Metrika 82:7, pages 863-889.
Crossref
V. B. Goryainov & E. R. Goryainova. (2019) Comparative Analysis of Robust and Classical Methods for Estimating the Parameters of a Threshold Autoregression Equation. Automation and Remote Control 80:4, pages 666-675.
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