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Einmahl, J. H. J., & He, Y. (2023). Extreme Value Estimation for Heterogeneous Data. Journal of Business & Economic Statistics, 41(1), 255-269. https://doi.org/10.1080/07350015.2021.2008408
He, Y., Jaidee, S., & Gao, J. (2023). Most powerful test against a sequence of high dimensional local alternatives. Journal of Econometrics, 234(1), 151-177. https://doi.org/10.1016/j.jeconom.2021.10.015
He, Y., Jaidee, S. & Gao, J. (1-1-2021). Replication codes for "Most powerful test against a sequence of high dimensional local alternatives". Universiteit van Amsterdam. https://doi.org/10.21942/uva.16822369.v1
2022
He, Y., Peng, L., Zhang, D., & Zhao, Z. (2022). Risk Analysis via Generalized Pareto Distributions. Journal of Business & Economic Statistics, 40(2), 852-867. https://doi.org/10.1080/07350015.2021.1874390
2020
He, Y., Hou, Y., Peng, L., & Shen, H. (2020). Inference for Conditional Value-at-Risk of a Predictive Regression. The Annals of Statistics, 48(6), 3442–3464. https://doi.org/10.1214/19-AOS1937[details]
2019
He, Y., Hou, Y., Peng, L., & Sheng, J. (2019). Statistical Inference for a Relative Risk Measure. Journal of Business and Economic Statistics, 37(2), 301-311. https://doi.org/10.1080/07350015.2017.1321549
2017
He, Y., & Einmahl, J. H. J. (2017). Estimation of extreme depth-based quantile regions. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 79(2), 449-461. https://doi.org/10.1111/rssb.12163
Talk / presentation
He, Y. (speaker) (19-12-2020). Unified extreme value estimation for heterogeneous data, 13th International Conference of the ERCIM WG on Computational and Methodological Statistics, London.
He, Y. (speaker) (15-5-2020). Most powerful test against high dimensional free alternatives, Netherlands Econometric Study Group Conference, Tilburg. http://www.nesg.nl/
Einmahl, J. H. J. & He, Y. (11-11-2021). Dataset for "Extreme Value Estimation for Heterogeneous Data". Universiteit van Amsterdam. https://doi.org/10.21942/uva.16988527.v1
He, Y., Jaidee, S. & Gao, J. (1-1-2021). Replication codes for "Most powerful test against a sequence of high dimensional local alternatives". Universiteit van Amsterdam. https://doi.org/10.21942/uva.16822369.v1
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