Research output per year
Research output per year
Personal profile
Personal profile
Guo Feifei Job Title: Assistant Professor Email: feifeiguo@bit.edu.cn
Tutor for postgraduate students. Ph. D., Hong Kong University of Science and Technology, postdoctoral fellow, School of Mathematics and Statistics, Beijing Institute of Technology. The research direction is nonlinear time series analysis, thick tail time series analysis and non-stationary time series analysis. He presided over a youth project of the National Natural Science Foundation.
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Tutor for postgraduate students. Ph. D., Hong Kong University of Science and Technology, postdoctoral fellow, School of Mathematics and Statistics, Beijing Institute of Technology. The research direction is nonlinear time series analysis, thick tail time series analysis and non-stationary time series analysis. He presided over a youth project of the National Natural Science Foundation.
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Research Interests
Mathematical statistics, statistical analysis of complex time series
Education
2009.09-2016.06 Bachelor of Science, Master of Science, Beijing Normal University (Supervisor: Professor Tong Xingwei)
2016.09-2020.10 Doctor of Statistics, Hong Kong University of Science and Technology (Supervisor: Professor Ling Shiqing)
2016.09-2020.10 Doctor of Statistics, Hong Kong University of Science and Technology (Supervisor: Professor Ling Shiqing)
Professional Experience
2023.01 - Now Assistant Professor, School of Mathematics and Statistics, Beijing Institute of Technology
2020.12-2022.12 Postdoc, School of Mathematics and Statistics, Beijing Institute of Technology, Co-supervisor: Professor Yubin Tian
2020.12-2022.12 Postdoc, School of Mathematics and Statistics, Beijing Institute of Technology, Co-supervisor: Professor Yubin Tian
Research Achievement
4、Guo, F. and Ling, S. (2020). Quasi-likelihood Estimation of Structure-changed Threshold Double Autoregressive Models, Journal of Statistical Planning and Inference./
3、Guo, F., Ling, S. and Mi, Z. (2022). Automated Estimation of Heavy-Tailed Vector Error Correction Models, Statistica Sinica.
2、Guo, F. and Ling, S. (2022). Inference for the VEC(1) Model with a Heavy-tailed Linear Process Errors, Econometric Reviews.
1、Guo, F. and Ling, S. (2023). Statistical Inference for Heavy-tailed and Partially Nonstationary Vector ARMA Models. Statistica Sinica.
3、Guo, F., Ling, S. and Mi, Z. (2022). Automated Estimation of Heavy-Tailed Vector Error Correction Models, Statistica Sinica.
2、Guo, F. and Ling, S. (2022). Inference for the VEC(1) Model with a Heavy-tailed Linear Process Errors, Econometric Reviews.
1、Guo, F. and Ling, S. (2023). Statistical Inference for Heavy-tailed and Partially Nonstationary Vector ARMA Models. Statistica Sinica.
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Collaborations and top research areas from the last five years
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Research output
- 3 Article
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Inference for the VEC(1) model with a heavy-tailed linear process errors*
Guo, F. & Ling, S., 2023, In: Econometric Reviews. 42, 9-10, p. 806-833 28 p.Research output: Contribution to journal › Article › peer-review
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AUTOMATED ESTIMATION of HEAVY-TAILED VECTOR ERROR CORRECTION MODELS
Guo, F., Ling, S. & Mi, Z., Oct 2022, In: Statistica Sinica. 32, 4, p. 2171-2198 28 p.Research output: Contribution to journal › Article › peer-review
2 Citations (Scopus) -
Quasi-likelihood estimation of structure-changed threshold double autoregressive models
Guo, F. & Ling, S., Mar 2020, In: Journal of Statistical Planning and Inference. 205, p. 138-155 18 p.Research output: Contribution to journal › Article › peer-review