Research areas

High Dimensional Statistics; Large Dimensional Random Matrix Theory; Functional Data Analysis; Responsible Statistical Learning

Biography

Yanrong Yang is an Associate Professor of Statistics. Yanrong’s research interests include high dimensional statistical inference and large dimensional random matrix theory. She has established new asymptotic theory for high dimensional statistics and applied her theory to forecasting high dimensional time series and large panel data. Yanrong has published in leading journals in statistics and actuarial science including Annals of Statistics, Journal of the Royal Statistical Society: Series B, Journal of the American Statistical Association, Journal of Econometrics, Econometric Theory, Annals of Applied Statistics, Statistica Sinica, Electronic Journal of Probability, Journal of the Royal Statistical Society: Series A, Journal of Multivariate Analysis, Insurance: Mathematics and Economics, Economics Letters, Journal of Statistical Planning and Inference, Australian and New Zealand Journal of Statistics, and Journal of Computational Finance.    

CV

Google Scholar

Grants

ARC DP260104297 (with Fei Huang and Samuel Muller): 2026 - 2029

Title: Responsible Statistical Learning: Uncertainty, Fairness and Transparency

ARC DP230102250 (with Hanlin Shang, Degui Li, Xinghao Qiao and Qingliang Fan): September 2023 - September 2026

Title: Feature learning for high-dimensional functional time series

Publications

Journal Articles

[20] Daning Bi, Hanlin Shang, Yanrong Yang, Huanjun Zhu (2026). AR-sieve Bootstrap for High-dimensional Time Series. https://arxiv.org/pdf/2112.00414 Accepted by Journal of Statistical Planning and Inference. 

[19] Ruike Wu, Yanrong Yang, Hanlin Shang, Huanjun Zhu. (2025). Making Uncertainty Learning Feasible on High-dimensional Portfolio Seleciton. https://arxiv.org/pdf/2405.16989 Accepted by Journal of Econometrics. 

[18] Daning Bi, Le Chang, Yanrong Yang. (2025).  Homogeneity and Sub-homogeneity Pursuit: Iterative Complement Clustering PCA. https://arxiv.org/abs/2203.06573 Accepted by Economics Letters. 

[17] Chen Tang, Hanlin Shang, Yanrong Yang, Yang Yang(2025). Multi-population mortality forecasting using high-dimensional functional factor models. https://arxiv.org/pdf/2109.04146.pdf Accepted by Journal of the Royal Statistical Society: Series A.   

[16] Bo Zhang, Jiti Gao, Guangming Pan, Yanrong Yang. (2025). Identifying the structure of high-dimensional time series via eigen-analysis. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3496388. Accepted by Journal of the American Statistical Association. 

[15] Bin Peng, Liangjun Su, Joakim Westerlund, Yanrong Yang. (2023). Interactive effects panel data models with general factors and regressors. Econometric Theory, 41, 472 - 488. https://arxiv.org/pdf/2111.11506.pdf. 

[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

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Under Review

[1] Qingliang Fan, Ruike Wu, Yanrong Yang (2025). Robust minimum variance portfolio for a large universe of assets. https://arxiv.org/pdf/2410.01826 

[2] Daning Bi, Xiao Han, Adam Nie, Yanrong Yang (2025). Spiked eigenvalues of high-dimensional sample auto-covariance matrices: CLT and its applications. https://arxiv.org/pdf/2201.03181.pdf 

[3] Qingliang Fan, Ruike Wu and Yanrong Yang (2025). Adaptive Multi-task Learning for Multi-sector Portfolio Optimization. https://arxiv.org/abs/2507.16433

[4] Yonghe Lu, Yanrong Yang and Terry Zhang (2025). Double Descent in Portfolio Optimization: Dance between Theoretical Sharpe Ratio and Estimation Accuracy. https://arxiv.org/pdf/2411.18830

[5] Fei Huang, Junhao Shen, Yanrong Yang and Ran Zhao (2025). Learning Fair Decision with Factor Models: Applications to Annuity Pricing. https://arxiv.org/pdf/2412.04663

[6] Houren Hong, Janice Scealy, Andrew Wood, Yanrong Yang (2025). A Robust Extrinsic Single-index Model for Spherical Data. https://arxiv.org/pdf/2503.24003

[7] Yonghe Lu, Yanrong Yang, Haijun Gong, Bo Zhang (2025). Change-Point Detection for Factor-Augmented Graphical Modelling on High-Dimensional Data. 

[8] Chuang Xu, Andrew Wood, Yanrong Yang (2025). Equivalence Test for Mean Functions From Multi-population Functional Data. https://arxiv.org/pdf/2509.24242

[9] Jiti Gao, Fei Liu, Bin Peng, Yanrong Yang (2025). Localized Neural Network Modelling of Time Series: A Case Study on US Monetary Policy. https://arxiv.org/abs/2306.05593

[10] Adam Nie, Yi He, Hanlin Shang, Yanrong Yang (2026). Spurious Factors for High-dimensional Functional Time Series. 

[11] Ruike Wu, Yonghe Lu, Yanrong Yang (2026). Sustainable Investment in Large Assets. https://arxiv.org/pdf/2602.14439

[12] Hanlin Shang, Yanrong Yang, Kehan Zhao (2026). Benign Overfitting in High-dimensional Linear Discriminant Analysis. 

Research engagement

Graduated PhD 

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PhD Supervision (completion, as primary supervisor)

Yonghe Lu (2021 - 2025): Covariance Inference for High-dimensional Complicated Data  

Yang Yang (2016 - 2020): Modelling and Forecasting for Functional Time Series 

Lingyu He (2016 - 2020): Data-adaptive Principal Component Analysis for High Dimensional Data

Yuan Gao (2015 - 2020): Modelling and Forecasting for High-dimensional Functional Data

Daning Bi (2016 - 2021): Various Statistical Inferences for High-dimensional time series

Chen Tang (2016 - 2021): Modelling and Forecasting High-dimensional Functional Time Series

PhD Supervision (completion, as associate supervisor)

Adam Nie (2018 - 2022): Spectral Analysis for High-dimensional Autocovariance Matrices

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Current PhD

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PhD Supervision (Current, as primary supervisor)

Ran Zhao (2025 - ): Fairness Learning in High-dimensional Statistics

PhD Supervision (Current, as associate supervisor)

Houren Hong (2021 - expected 2026 March): Semi-parametric Inference for Spherical Data

Chuang Xu (2024 - expected 2028): Statistical Inference for Multivariate Functional Data

Linbin Wang (at Macquarie University, 2026.01 - ): Statistical Inference for Spatial-temporal Functional Time Series

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Graduated Honours Students

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Honours Student Supervision (completion, as primary supervisor)

Linbin Wang (2025.01 - 2025.10): ESG-guided Responsible Investment on Large Assets

Kehan Zhao (2023.01 - 2023.10): Double Descent in High-dimensional LDA

James Walter (2023.01 - 2023.10): Double Descent in Large Portfolio Theory

Lu Wang (2020.07 - 2021.06): Kernel PCA for High-dimensional Data

Ruofan Xu (2019.07 - 2020.06): Spatial-temporal Functional Data Analysis

Qibin Zhu (2017.01 - 2017.10): Regularised Interactive-effect Panel Data Modelling and Inference

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Current Honours Students

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Honours Student Supervision (current and future, as primary supervisor)

Caitlin Xu (2025.07 - expected 2026.06): Privacy-perserving Learning for Generalised Linear Modelling and Application to Car Insurance

Will Brake (2026.01 - expected 2026.10): AI-guided Regulatory on Large Portfolio Theory 

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Teaching

Current Teaching:

STAT3050/4050/7050 Advanced Statistical Learning

Previous Teaching:

STAT3013/4027/8027 Statistical Inference

STAT3040/4040/7040 Statistical Learning

STAT3017/6017 Big Data Statistics