Dr. Jinyuan Chang School of Statistics Southwestern University of Finance and Economics Area B, Tongbo Building, 555 Liutai Avenue, Wenjiang District, Chengdu, Sichuan Province, China 611130 Email: changjinyuan [AT] swufe [DOT] edu [DOT] cn Biography I was born in Chongqing and raised in Chengdu, China. I had my senior middle school education at Chengdu No. 7 High school. Then, I attended School of Mathematical Science at Beijing Normal University in 2005 and got my Bachelor's degree of Science in Statistics in June 2009. Then, I went to Guanghua School of Management at Peking University for PhD study under the supervision of Prof. Song Xi Chen. I obtained the Doctor degree of Philosophy in Economics in July 2013. From September 2013 to Feburary 2017, I had a research fellow position in Department of Mathematics & Statistics at the University of Melbourne under supervision of the late Prof. Peter Hall. Now, I am an Associate Professor of Statistics and Econometrics at Southwestern University of Finance and Economics. Research Interests
Professional Servies
Professional Memberships
Research Papers PeerReviewed 10. Chang, J., Zheng, C., Zhou, W.X. & Zhou, W. (2017). Simulationbased hypothesis testing of high dimensional means under covariance heterogeneity, Biometrics, in press. Available at arXiv:1406.1939. 9. 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, Vol. 73, 31–41. (Original title "Bootstrap tests on high dimensional covariance matrices with applications to understanding gene clustering") 8. Chang, J., Yao, Q. & Zhou, W. (2017). Testing for highdimensional white noise using maximum crosscorrelations, Biometrika, Vol. 104, pp. 111–127. 7. Chang, J.,
Shao, Q.M. & Zhou, W.X. (2016). Cram\´ertype moderate deviations for Studentized twosample Ustatistics with applications, The Annals of Statistics, Vol. 44, pp. 1931–1956. 6. Chang, J.,
Tang, C. Y. & Wu, Y. (2016). Local independence feature screening for nonparametric and semiparametric models by marginal empirical likelihood, The Annals of Statistics, Vol. 44, pp. 515–539. 5. Chang, J., Guo, B. & Yao, Q. (2015). High dimensional stochastic regression with latent factors, endogeneity and nonlinearity, Journal of Econometrics, Vol. 189, pp. 297–312. 4. Chang, J. & Hall, P. (2015). Doublebootstrap methods that use a single doublebootstrap simulation, Biometrika, Vol. 102, pp. 203–214. 3. Chang, J., Chen, S. X. & Chen, X. (2015). High dimensional generalized empirical likelihood for moment restrictions with dependent data, Journal of Econometrics, Vol. 185, pp. 283–304. 2. Chang, J., Tang, C. Y. & Wu, Y. (2013). Marginal empirical likelihood and sure independence feature screening, The Annals of Statistics, Vol. 41, pp. 2123–2148. 1. Chang, J. & Chen, S. X. (2011). On the approximate maximum likelihood estimation for diffusion processes, The Annals of Statistics, Vol. 39, pp. 2820–2851. Review Papers & Invited Discussion 1. Chang, J., Guo, J. & Tang, C. Y. (2017). Peter Hall's contribution to empirical likelihood, Statistica Sinica, in press. Under Review 1. Chang, J., Guo, B. & Yao, Q. (2014). Principal component analysis for secondorder stationary vector time series. 2. Chang, J., Qiu, Y., Yao, Q. & Zou, T. (2016). On the statistical inference for large precision matrices with dependent data. 3. Chang, J., Delaigle, A., Hall, P. & Tang, C. Y. (2016). A frequency domain analysis of highfrequency financial data. 4. Chang, J., Tang, C. Y. & Wu, T. T. (2017). A new scope of penalized empirical likelihood with highdimensional estimating equations.
Fellowships and Honors
