姓名：王德辉
职称：教授、院长
研究方向：数理统计、时间序列分析、风险理论
开设课程：概率论与数理统计、时间序列分析、精算模型、现代精算风险理论、计量经济学、统计学、高等数理统计、非线性时间序列分析
电子邮箱：wangdehui@lnu.edu.cn
个人简历
王德辉，男，汉族，1969年1月出生，教授、博士生导师、辽宁大学数学与统计学院院长、统计学科带头人、享受国务院政府津贴专家、宝钢优秀教师奖获得者、教育部新世纪优秀人才、吉林省长白山学者特聘教授、高等学校统计学类专业教学指导委员会委员（2013-2022），吉林省第四批高级专家，吉林省高等学校首批学科领军教授、吉林省第六批拔尖创新人才第一层次人选 、吉林省“第十二批有突出贡献的中青年专业技术人才、长春市有突出贡献专家等。
教育及工作经历：
• 1998.9 - 2001.6，吉林大学，概率论与数理统计专业攻读博士学位，师从于史宁中教授。
• 1995.9 - 1998.6，吉林大学，概率论与数理统计专业攻读硕士学位，师从于宋立新教授。
• 1989.9 -1993.6，四平师范学院，数学教育专业攻读学士学位。
• 2001.7 – 2003.6，四川大学数学学院博士后，合作导师:朱允民教授。
• 2003.7 - 2007.12，吉林大学生物与农业工程学院博士后，合作导师：任露泉院士。
•1993.7 – 1998.6, 四平师范学院数学学院专任教师。
• 1998.6 – 2021.2，吉林大学数学学院专任教师。
• 2021.2 – 2022.2, 辽宁大学经济学院专任教师。
• 2022.2 - 现在,辽宁大学数学与统计学院专任教师。
主要成果：
主要从事时间序列分析、风险理论分析、保险精算等方面的研究，发表 SCI论文40余篇，主持(包括结题)国家自然科学基金面上项目7 项（含国家自然科学基金重点项目子项目1项），博士学科点专项基金1项，获得“2015年度高等学校科学研究优秀成果奖（科学技术）教育部自然科学二等奖”、“第十一届全国统计科研优秀成果二等奖”、“吉林省自然科学技术成果二等奖”、“吉林省自然科学技术成果三等奖”、2019年获吉林省科学技术奖二等奖(自然科学)各一项。
1. 科研项目：
（1） 《约束下时间序列模型统计推断》(10571073)》2006.1-2008.12，国家自然科学基金面上项目。
（2） 《教育部新世纪优秀人才支持计划(NECT－08－237)》，2009.01-2011.12，教育部。
（3） 《相依误差下时间序列模型的统计推断(10971081)》，2010.01-2012.12，国家自然科学基金面上项目。
（4） 《整数值时间序列数据的建模方法研究(11271155)》，2013.01-2016.12，国家自然科学基金面上项目 。
（5） 《长白山学者特聘教(2015010）》，2016.01-2019.12，吉林省人民政府。
（6） 《非平稳与高频时间序列模型的统计推断(11731015)》，2018.01-2022.12 国家自然科学基金重点项目（参与）。
（7） 《协变量驱动的随机系数自回归模型的统计推断(11871028)》，2019.01-2022.12 国家自然科学基金面上项目。
（8） 《复杂数据驱动的整值时间序列建模方法研究（12271231）》，2023.01-2026.12 国家自然科学基金面上项目。
2. 代表性论文：
[1] Rui Zhang, Dehui Wang* and Cong Li (2022). Flexible binomial AR(1) processes using copulas. Journal of Statistical Planning and Inference, 219: 306-332.
[2] Cong Li, Haixiang Zhang, and Dehui Wang (2022). Modelling and monitoring of INAR(1) process with geometrically inflated Poisson innovations. Journal of Applied Statistics, 49(7): 1821-1847.
[3] Feilong Lu, Dehui Wang* (2022). A new estimation for INAR(1) process with Poisson distribution. Computational Statistics,37: 1185–1201.
[4] Feilong, Lu, Dehui Wang* (2022). First order integer valued autoregressive process with Markov switching coefficients. Communications in Statistics - Theory and Methods, 51(13): 4313-4329.
[5] Xiufang Liu, Hao Jiang, Dehui Wang* (2021). Estimation of parameters in the MDDRCINAR(p) model. Journal of Statistical Computation and Simulation,50(24): 6231-6255.
[6] Dehui Wang, Shuai Cui, Jianhua Cheng, Shuhui Wang (2021). Statistical inference for the covariates-driven Binomial AR(1) process. Acta Mathematicae Applicatae Sinica, English Series, 37(4): 758-772.
[7] Tongwei Zhang, Dehui Wang*, and Kai Yang (2021). Quasi-maximum exponential likelihood estimation for double-threshold GARCH models. The Canadian Journal of Statistics, 49(4): 1152-1178.
[8] Kai Yang, Han Li*, Dehui Wang, and Chenhui Zhang (2021). Random coefficients integer-valued threshold autoregressive processes driven by logistic regression. AStA Advances in Statistical Analysis, 105(4): 533–557.
[9] Yao Kang, Dehui Wang* and Jianhua Cheng (2021). Risk models based on copulas for premiums and claim sizes. Communications in Statistics - Theory and Methods, 50(10): 2250-2269.
[10] Xiaohong Wang, Dehui Wang*, Kai Yang and 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): 1622–1644.
[11] Xinyang Wang, Dehui Wang*, and Kai Yang (2021). Integer-valued time series model order shrinkage and selection via penalized quasi-likelihood approach. Metrika, 84:713–750.
[12] Danshu Sheng, Dehui Wang*, Kai Yang, and Zi-ang, Wu (2021). Quantile regression for thinning-based INAR(1) models of time series of counts. Acta Mathematicae Applicatae Sinica, English Series, 37: 264–277.
[13] Yao Kang, Dehui Wang* and Kai Yang (2021). A new INAR(1) process with bounded support for counts showing equidispersion, underdispersion and overdispersion. Statistical Papers, 62:745–767.
[14] Xiong Wei, Dehui Wang* and Wang Xinyang (2020). Imputation-based semiparametric estimation for INAR(1) processes with missing data. Hacettepe Journal of Mathematics and Statistics, 49(5): 1843–1864.
[15] Qingchun, Zhang, Dehui Wang*, and Xiaodong, Fan (2020). A negative binomial thinning-based bivariate INAR(1) process. Statistica Neerlandica, 74(4): 517–537.
[16] Shengqi Tian, Dehui Wang* and Shuai Cui (2020). A seasonal geometric INAR(1) process based on negative binomial thinning operator. Statistical Papers, 61: 2561–2581.
[17] Yaodong Sun, and Dehui Wang* (2020). L1-Estimation for covariate-adjusted regression. Journal of Inequalities and Applications, 2020, 2020:75.
[18] XinyangWang, Dehui Wang*, and Haixiang Zhang (2020). Poisson autoregressive process modeling via the penalized conditional maximum likelihood procedure. Statistical Papers, 61, 245–260.
[19] Yao Kang, Dehui Wang*, Kai Yang and Yulin Zhang (2020). A new thinning-based INAR(1) process for underdispersed or overdispersed counts. Journal of the Korean Statistical Society, 49: 324–349.
[20] Yao Kang, Dehui Wang* and Kai Yang (2020). Extended binomial AR(1) processes with generalized binomial thinning operator. Communications in Statistics - Theory and Methods, 49(14): 3498–3520.
[21] Jie Zhang, Dehui Wang* and Kai Yang (2020). A study of RCINAR (1) process with generalized negative binomial marginals. Communications in Statistics - Simulation and Computation, 49(6): 1487–1510.
[22] Jie Zhang, Dehui Wang*, Kai Yang, and Yanju Xu (2020). A multinomial autoregressive model for finite-range time series of counts. Journal of Statistical Planning and Inference, 207: 320–343.
[23] Meiju Yu, Dehui Wang*, Kai Yang, and Yan Liu (2020). Bivariate first-order random coefficient integer-valued autoregressive processes. Journal of Statistical Planning and Inference, 204: 153–176.
[24] Kai Yang, Yao Kang, Dehui Wang*, Han Li, and Yajing Diao (2019). Modeling overdispersed or underdispersed count data with generalized Poisson integer-valued autoregressive processes. Metrika, 82(7): 863–889.
[25] Cong Li, Dehui Wang*, and Jianguo Sun (2019). Control charts based on dependent count data with deflation or inflation of zeros. Journal of Statistical Computation and Simulation, 89(17): 3273–3289.
[26] Xiufang Liu and Dehui Wang* (2019). Estimation of parameters in the DDRCINAR(p) model. Brazilian Journal of Probability and Statistics, 33(3): 638–673.
[27] Cong Li, Dehui Wang* and Fukang Zhu (2019). Detecting mean increases in zero truncated INAR(1) processes. International Journal of Production Research, 57(17): 5589–5603.
[28] Meiju Yu, Dehui Wang* and Kai Yang (2019). A class of observation-driven random coefficient INAR(1) processes based on negative binomial thinning. Journal of the Korean Statistical Society, 48(2): 248–264.
[29] Yanqiu Yang, Dehui Wang* and Zhiwen Zhao (2018). Empirical likelihood for first-order mixed integer-valued autoregressive model. Applied Mathematics - A Journal of Chinese Universities, 33(3): 313–322.
[30] Kai Yang, Dehui Wang*, Boting Jia and Han Li (2018). An integer-valued threshold autoregressive process based on negative binomial thinning. Statistical Papers, 59(3): 1131–1160.
[31] Kai Yang, Han Li and Dehui Wang* (2018). Estimation of parameters in the self-exciting threshold autoregressive processes for nonlinear time series of counts. Applied Mathematical Modelling, 57, 226-247.
[32] Han Li, Kai Yang, Shishun Zhao and Dehui Wang* (2018). First-order random coefficients integer-valued threshold autoregressive processes. AStA Advances in Statistical Analysis, 102(3): 305–331.
[33] Shihang Yu, Dehui Wang* and Xia Chen (2018). Large and moderate deviations for the total population arising from a sub-critical Galton-Watson process with immigration. Journal of Theoretical Probability, 31(1): 41–67.
[34] Kai Yang*, Dehui Wang and Han Li (2018). Threshold autoregression analysis for finite-range time series of counts with an application on measles data. Journal of Statistical Computation and Simulation, 88(3): 597–614.
[35] Han Li, Kai Yang and Dehui Wang* (2017). Quasi-likelihood inference for self-exciting threshold integer-valued autoregressive processes. Computational Statistics, 32(4): 1597–1620.
[36] Shuo Zhang, Dehui Wang* and Shihang Yu (2017). Precise large deviations of aggregate claims in a size-dependent renewal risk model with stopping time claim-number process. Journal of Inequalities and Applications, (2017) 2017: 82.
[37] Yanfeng Li, HuiJuan Ma, Dehui Wang* and Yong Zhou (2017). Analyzing the general biased data by additive risk model. Science China Mathematics, 60(4): 685–700.
[38] Zhiwen Zhao*, Dehui Wang and Cuixin Peng (2017). Conditional heteroscedasticity test for Poisson autoregressive model. Communications in Statistics - Theory and Methods, 46(9): 4437–4448.
[39] Xue Ding and Dehui Wang* (2016). Empirical likelihood inference for INAR(1) model withexplanatory variables. Journal of the Korean Statistical Society, 45(4): 623–632.
[40] Xin Qi and Dehui Wang* (2016). Estimation in a partially linear single-index model with missing response variables and error-prone covariates. Journal of Inequalities and Applications, (2016) 2016:11.
[41] Da Xu, Jianguo Sun* and Dehui Wang (2016). Nonparametric comparison of recurrent event processes based on panel count data. Journal of the Korean Statistical Society, 45(2): 250–259.
[42] Dongxing Ma and Dehui Wang* (2015). Bidimensional discrete-time risk models based on bivariate claim count time series. Journal of Inequalities and Applications, (2015) 2015:105.
[43] Fukang Zhu* and Dehui Wang (2015). Empirical likelihood for linear and log-linear INGARCH models. Journal of the Korean Statistical Society, 44(1): 150–160.
[44] Cong Li, Dehui Wang and Fukang Zhu* (2015). Effective control charts for monitoring the NGINAR(1) process. Quality and Reliability Engineering International, 32(3): 877–888.
[45] Haixiang Zhang and Dehui Wang* (2015). Inference for random coefficient INAR(1) process based on frequency domain analysis. Communications in Statistics - Simulation and Computation. 44(4): 1078-1100.
[46] Ying Wang, Dehui Wang*, Fukang Zhu (2014). Estimation of parameters in the fractional compound Poisson Process. Communications in Nonlinear Science and Numerical Simulation, 19(10): 3425–3430.
[47] Shihang Yu, Dehui Wang* (2014). Empirical likelihood for first-order autoregressive error-in-explanatory variable models with validation data. Communications in Statistics-Theory and Methods, 43(8): 1800–1823.
[48] Boting Jia and Dehui Wang* (2014). A Study for Missing Values in PINAR(1)T Processes. Communications in Statistics-Theory and Methods, 43(22): 4780–4789.
[49] Haifang Shi and Dehui Wang* (2014). An approximation model of the collective risk model with INAR(1) claim process. Communications in Statistics- Theory and Methods, 43(24): 5305–5317.
[50] Zhiwen Zhao, Dehui Wang and Cui-Xin Peng (2015). Test for parameter changes in generalized random coefficient autoregressive model. Journal of Inequalities and Applications 2014: 309.
[51] Jianhua Cheng, Dehui Wang* (2013). On a perturbed MAP risk model under a threshold dividend strategy. Journal of the Korean Statistical Society, 42(4): 543–564.
[52] Zhuoxi Yu, ShiShun Zhao, Dehui Wang* (2013). Empirical likelihood inference for partial linear models with ARCH(1) errors, Mathematical and Computer Modelling, 57(9-10): 2449–2458.
[53] Zhiwen Zhao, Dehui Wang and Cuixin Peng (2013). Coefficient constancy test in generalized random coefficient autoregressive model. Communications in Statistics- Theory and Methods, 219(20): 10283–10292.
[54] Jianhua Cheng and Dehui Wang* (2011). Ruin problems for an autoregressive risk model with dependent rates of interest. Applied Mathematics and Computation, 218(7): 3822–3833.
[55] Haixiang Zhang, Dehui Wang* and Fukang Zhu (2011). Empirical likelihood inference for random coefficient INAR(p) process. Journal of Time Series Analysis, 32(3): 195–203.
[56] Fukang Zhu and Dehui Wang* (2011), Estimation and testing for a Poisson autoregressive model. Metrika (2011) 73:211–230.
[57] Zhiwen Zhao, Dehui Wang and Yong Zhang (2011). Limit theory for random coefficient first-order autoregressive process under martingale difference error sequence. Journal of Computational and Applied Mathematics, 235(8): 2515–2522.
[58] Dehui Wang* and Haixiang Zhang (2010). Generalized RCINAR(p) process with signed thinning operator. Communications in Statistics - Simulation and Computation, 40(1): 13–44.
[59] Fukang Zhu,QiLi and Dehui Wang* (2010). A mixture integer-valued ARCH model. Journal of Statistical Planning and Inference, 140(7): 2025–2036.
[60] Haixiang Zhang, Dehui Wang* and Fukang Zhu (2010). Inference for INAR(p) processes with signed generalized power series thinning operator. Journal of Statistical Planning and Inference, 140(7): 667–683.
[61] Fukang Zhu and Dehui Wang* (2010), Diagnostic checking integer-valued ARCH.p models using conditional residual autocorrelations. Computational Statistics and Data Analysis, 54: 496–508.
[62] Zhuoxi Yu, Dehui Wang and Ningzhong Shi (2009). Semiparametric estimation of regression functions in autoregressive models. Statistics and Probability Letters, 79(2): 165–172.
[63] Fukang Zhu and Dehui Wang (2008). Estimation of parameters in the NLAR(p) model. Journal of Time Series Analysis, 29(4): 619–628.
[64] Fukang Zhu and Dehui Wang (2008). Local estimation in AR models with nonparametric ARCH errors. Communications in Statistics - Theory and Methods, 37(8-10): 1591–1609.
[65] Dehui Wang, Lixin Song and Ningzhong Shi (2004). Estimation and testing for the parameters of ARCH(q) under ordered restriction. Journal of Time Series Analysis. 25(4): 483–499.
[66] Ningzhong Shi and Dehui Wang (2003). Median unbiased and maximum likelihood estimations of ARCH(0,1) coefficient. Communications in Statistics - Theory and Methods, 32(5): 1057–1066.
主要社会兼职：
1. 中国现场统计研究会资源与环境统计分会 副理事长（2021.10-2025.10）。
2. 全国工业统计学教学研究会 副会长（2018.12-2022.12）。
3. 中国现场统计研究会常务理事（2021.11-2025.11）
