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2024.12.06

Representative Work Series 3: New Theories of Statistical Inference Based on Gaussian Approximation

Chang, J., Jiang, Q., McElroy, T., & Shao, X. (2025+). Statistical inference for high-dimensional spectral density matrix. Journal of the American Statistical Association, in press.
Chang, J., Hu, Q., Kolaczyk, E. D., Yao, Q., & Yi, F. (2024). Edge differentially private estimation in the β-model via jittering and method of moments. Annals of Statistics, 52, 708-728.
Chang, J., Chen, X., & Wu, M. (2024). Central limit theorems for high dimensional dependent data. Bernoulli, 30, 712-742.
Chang, J., He, J., Kang, J., & Wu, M. (2024). Statistical inferences for complex dependence of multimodal imaging data. Journal of the American Statistical Association, 119, 1486-1499.
Chang, J., Jiang, Q., & Shao, X. (2023). Testing the martingale difference hypothesis in high dimension. Journal of Econometrics, 235, 972-1000.
Chang, J., Qiu, Y., Yao, Q., & Zou, T. (2018). Confidence regions for entries of a large precision matrix. Journal of Econometrics, 206, 57-82.
Chang, J., Yao, Q., & Zhou, W. (2017). Testing for high-dimensional white noise using maximum cross-correlations. Biometrika, 104, 111-127.
Chang, J., Zheng, C., Zhou, W.-X., & Zhou, W. (2017). Simulation-based hypothesis testing of high dimensional means under covariance heterogeneity. Biometrics, 73, 1300-1310.
Chang, J., Zhou, W., Zhou, W.-X., & Wang, L. (2017). Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering. Biometrics, 73, 31-41.

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