Jing Zhou

Jing Zhou is a Lecturer in Statistics at The University of Manchester, Department of Mathematics. Her research focuses on statistical methods, with an emphasis on hypothesis testing, quantile regression, and high-dimensional statistical inference. She has contributed to the development of statistical techniques for analyzing complex data structures, including work on asymptotic theory and test statistics. Zhou's research has been published in peer-reviewed journals and presented at academic conferences. She is also involved in the supervision of PhD students and the dissemination of statistical knowledge through various platforms.

Jing Zhou is a Lecturer in Statistics at the University of Manchester, specializing in high dimensional statistics and robust methods. Their research focuses on developing statistical techniques for handling complex data structures, particularly in high-dimensional settings. Zhou's work includes quantile regression, composite estimation, and model averaging, with an emphasis on improving robustness and reducing false discovery rates. They have also explored the application of model-X knockoffs in social science research to mitigate researcher bias and enhance variable selection. Their publications highlight methodological advancements in statistical inference and the use of computational tools for data analysis.