学术论文： |
部分代表性论文： [1]. 张冰雨, 朱复康 (2025). 网络GARCH(1,1)模型的分位回归估计. 中国科学-数学, 已接受. [2]. Xu, Y. and Zhu, F. (2025). A zero-inflated Poisson asymmetric power GARCH model for Z-valued time series. Communications in Mathematics and Statistics, forthcoming. [3]. Qian, L. and Zhu, F. (2025). A flexible model for time series of counts with overdispersion or underdispersion, zero-inflation and heavy-tailedness. Communications in Mathematics and Statistics, forthcoming. [4]. Weiß, C.H. and Zhu, F. (2025). Mean-preserving rounding integer-valued ARMA models. Journal of Time Series Analysis, forthcoming. [5]. Chen, H., Han, Z. and Zhu, F. (2025). A trinomial difference autoregressive process for the bounded Z-valued time series. Journal of Time Series Analysis, forthcoming. [6]. Weiß, C.H. and Zhu, F. (2025). Tobit models for count time series. Scandinavian Journal of Statistics, forthcoming. [7]. Pei, J., Lu, Y. and Zhu, F. (2024). Mixed causal-noncausal count process. TEST, forthcoming. [8]. Guo, X. and Zhu, F. (2024). Negative binomial community network vector autoregression for multivariate integer-valued time series. Applied Mathematical Modelling, 134, 713-734. [9]. Su, Z., Zhu, F. and Liu, S. (2024). Local influence analysis in the softplus INGARCH model. TEST, 33(3), 951-985. [10]. Kang, Y., Zhu, F., Wang, D. and Wang, S. (2024). A zero-modified geometric INAR(1) model for analyzing count time series with multiple features. Canadian Journal of Statistics, 52(3), 873-899. [11]. Kang, Y., Wang, S., Wang, D. and Zhu, F. (2023). Analysis of zero-and-one inflated bounded count time series with applications to climate and crime data. TEST, 32(1), 34-73. [12]. Zhu, F., Liu, M., Ling, S. and Cai, Z. (2023). Testing for structural change of predictive regression model to threshold predictive regression model. Journal of Business & Economic Statistics, 41(1), 228-240. [13]. Xiong, L. and Zhu, F. (2022). Minimum density power divergence estimator for negative binomial integer-valued GARCH models. Communications in Mathematics and Statistics, 10(2), 233-261. [14]. Xu, Y. and Zhu, F. (2022). A new GJR-GARCH model for Z-valued time series. Journal of Time Series Analysis, 43(3), 490–500. [15]. Liu, M., Zhu, F. and Zhu, K. (2022). Modeling normalcy-dominant ordinal time series: An application to air quality level. Journal of Time Series Analysis, 43(3), 460-478. [16]. Weiß, C.H., Zhu, F. and Hoshiyar, A. (2022). Softplus INGARCH models. Statistica Sinica, 32(2), 1099-1120. [17]. Liu, M., Zhu, F. and Zhu, K. (2022). Multifrequency-band tests for white noise under heteroskedasticity. Journal of Business & Economic Statistics, 40(2), 799-814. [18]. Liu, Z., Li, Q. and Zhu, F. (2021). Semiparametric integer-valued autoregressive models on Z. Canadian Journal of Statistics, 49(4), 1317-1337. [19]. Qian, L., Li, Q. and Zhu, F. (2020). Modelling heavy-tailedness in count time series. Applied Mathematical Modelling, 82, 766-784. [20]. Cui, Y. and Zhu, F.(2018). A new bivariate integer-valued GARCH model allowing for negative cross-correlation. TEST, 27(2), 428-452. [21]. Ling, S., Peng, L. and Zhu, F. (2015). Inference for a special bilinear time-series model. Journal of Time Series Analysis, 36(1), 61-66. [22]. Zhu, F., Cai, Z. and Peng, L. (2014). Predictive regressions for macroeconomic data. Annals of Applied Statistics, 8(1), 577-594. (SSCI) [23]. Zhang, H., Wang, D. and Zhu, F. (2011). Empirical likelihood inference for random coefficient INAR(p) process. Journal of Time Series Analysis, 32(3), 195-203. [24]. Zhu, F. (2011). A negative binomial integer-valued GARCH model. Journal of Time Series Analysis, 32(1), 54-67. [25]. Zhu, F. and Wang, D. (2008). Estimation of parameters in the NLAR(p) model. Journal of Time Series Analysis, 29(4), 619-628. |
