Dr Yanrong Yang

PhD Nanyang Technological University, Singapore
Senior Lecturer
ANU College of Business and Economics
T: +61 2 612 58975

Areas of expertise

  • Statistics 0104
  • Econometrics 1403

Research interests

High Dimensional Statistics 

Large Panel Data Analysis

Large Dimensional Random Matrix Theory

Statistical Analysis on Mortality Forecasting



Work Experience

Senior Lecturer (2020.1-Current): The Australian National University, Australia

Lecturer (2016.7-2019.12): The Australian National University, Australia

Post-doctor (2013.8-2016.7): Monash University, Australia


PhD (2009.8-2013.4): Nanyang Technological University, Singapore

MSc (2006.9-2009.7): Shandong University, China

Bachelor (2002.9-2006.7): Shandong University, China



Researcher's projects

Journal Articles

[14] Lingyu He, Yanrong Yang, Bo Zhang (2022). Robust PCA for high-dimensional data based on characteristic function. Under Review. Australian and New Zealand Journal of Statistics. Accepted.  

[13] Qingliang Fan, Ruike Wu, Yanrong Yang, Wei Zhong. (2022). Time-varying minimum variance portfolio. Journal of Econometrics. To appear. https://doi.org/10.1016/j.jeconom.2022.08.007

[12] Yuan Gao, Han Lin Shang, Yanrong Yang (2022). Factor-augmented smoothing model for functional data. Statistica Sinica. To appear. https://doi.org/10.5705/ss.202021.0223

[11] Chen Tang, Han Lin Shang, Yanrong Yang (2022). Clustering and forecasting multiple functional time series. Annals of Applied Statistics. 16(4), 2523 - 2553.  

[10] Yang Yang, Yanrong Yang, Han Lin Shang (2021). Feature extraction for functional time series: theory and application to NIR spectroscopy data. Journal of Multivariate Analysis. 189, 104863.

[9] Xiaoyi Han, Bin Peng, Yanrong Yang, Huanjun Zhu (2021). Shrinkage estimation of the varying-coefficient model with continuous and categorical covariates. Economics Letters. 202, 109819. 

[8] Jianjie Shi, Lingyu He, Fei Huang, Yanrong Yang (2021). Mortality forecasting: time-varying or constant factor loadings? Insurance: Mathematics and Economics. 98, 14-34. 

[7] Bin Jiang, Yanrong Yang, Jiti Gao, Cheng Hsiao (2021). Recursive estimation in large panel data models: theory and practice. Journal of Econometrics. 224(2) 439-465.

[6] Yuan Gao, Hanlin Shang, Yanrong Yang (2019). High dimensional functional time series analysis: an application to age-specific mortality rates. Journal of Multivariate Analysis. 170, 232-243.

[5] Jiti Gao, Xiao Han, Guangming Pan, Yanrong Yang (2017). High dimensional correlation matrices: CLT and its applications. Journal of the Royal Statistical Society: Series B. 79(3) 677-693.

[4] Yanrong Yang, Guangming Pan (2015). Independence test for high dimensional data based on regularized canonical correlation coefficients. Annals of Statistics. 43(2) 467-500.

[3] Guangming Pan, Jiti Gao, Yanrong Yang (2014). Test independence among a large number of high dimensional random vectors. Journal of the American Statistical Association. 109(506) 600-612.

[2] Yanrong Yang, Guangming Pan (2012). The convergence of the empirical distribution of canonical correlation coefficients. Electronic Journal of Probability. 17(64) 1-13.

[1] Haifeng Fu, Xin Jin, Guangming Pan, Yanrong Yang (2012). Estimating multiple option greeks simultaneously using random parameter regression. Journal of Computational Finance. 16(2), 85-118.

Book Chapters

[2] Gao, Yuan, Shang, Han Lin, Yang, Yanrong (2020). ‘Modelling functional data with high-dimensional error structure’, Functional and High-Dimensional Statistics and Related Fields, Springer, pp. 99-106

[1] Gao, Yuan, Shang, Han Lin, Yang, Yanrong (2017). ‘High-dimensional functional time series forecasting’, Functional Statistics and Related Fields, Springer, pp. 131-136



[9] Daning Bi, Le Chang, Yanrong Yang (2022). Homogeneity and sub-homogeneity pursuit: iterative complement clustering PCA. https://arxiv.org/pdf/2203.06573.pdf . 

[8] Daning Bi, Xiao Han, Adam Nie, Yanrong Yang (2022). Spiked eigenvalues of high-dimensional sample auto-covariance matrices: CLT and its applications. https://arxiv.org/pdf/2201.03181.pdf , Under Review.  

[7] Daning Bi, Han Lin Shang, Yanrong Yang, Huanjun Zhu. (2021). AR-sieve bootstrap for high-dimensional time series.   https://arxiv.org/pdf/2112.00414.pdf Under Review. 

[6] Chen Tang, Han Lin Shang, Yanrong Yang. (2021). Multi-population mortality forecasting using high-dimensional functional factor models. https://arxiv.org/pdf/2109.04146.pdf Under Review.

[5] Bin Peng, Liangjun Su, Joakim Westerlund, Yanrong Yang. (2021). Interactive effects panel data models with general factors and regressors. https://arxiv.org/pdf/2111.11506.pdf Under Revision. 

[4] Fei Liu, Jiti Gao, Yanrong Yang. (2020). Time-varying panel data models with an additive factor structure. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3729869 Under Revision. 

[3] Lingyu He, Fei Huang, Yanrong Yang. (2020). A forecast-driven hierarchical factor model with application to mortality data. https://arxiv.org/pdf/2102.04123.pdf Under Revision. 

[2] Bo Zhang, Jiti Gao, Guangming Pan, Yanrong Yang. (2020). Spiked eigenvalues of high-dimensional separable sample covariance matrices. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3496388 Under Revision.

[1] Jiti Gao, Guangming Pan, Yanrong Yang, Bo Zhang. (2019). Estimation of cross-sectional dependence in large panels. https://arxiv.org/pdf/1904.06843.pdf Under Revision. 


Working Papers:

[2] Yonghe Lu, Haijun Gong, Yanrong Yang, Bo Zhang. (2022). Change-point analysis for graphical modelling on ultra high-dimensional nonstationary time series. 

[1] Yuan Gao, Han Lin Shang, Yang Yang, Yanrong Yang. (2022). Factor analysis for functional time series: cross-sectional dependence or temporal dependence?



Current student projects

Time-varying Large Panel Data Models with Interactive Effects

Doctor Philosophy--Associate Supervisor

Homogeniety Pursuit in Large Panel Data Models

Doctor Philosophy--Associate Supervisor


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Updated:  28 November 2022 / Responsible Officer:  Director (Research Services Division) / Page Contact:  Researchers