Cheng Granted CAREER Award - Department of Statistics - Purdue University Skip to main content

Cheng Granted CAREER Award


Assistant Professor Guang Cheng has been awarded a grant by the Faculty Early Career Development (CAREER) Program from the Division of Mathematical Sciences of the National Science Foundation (NSF). This prestigious award supports junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education, and the integration of education and research, to build a firm foundation for a lifetime of leadership in integrating education and research.

Guang's Award: "CAREER: Bootstrap M-estimation in Semi-Nonparametric Models" is funded from July 1, 2012 to June 30, 2017 with a total budget of $400K. His research will focus on the issues associated with the rapid advancement of modern computers and technologies that have created unprecedented data explosion. The immediate need for fast and efficient extraction of information from massive data gives rise to the increasing popularity of the semi-nonparametric models. For example, to understand the recent financial crisis, the semi-nonparametric copula models are applied to address tail dependence among shocks to different financial series and also to recover the shapes of the impact curve for individual financial series.

As a general-purpose approach to statistical inferences, the bootstrap has found wide applications in semi-nonparametric models. Unfortunately, systematic theoretical studies on the bootstrap inferences are extremely limited, especially when the nonparametric component is not root-n estimable. To address these issues, Dr. Cheng proposes bootstrap inferential strategies for two broad classes of bootstrap methods in the context of semi-nonparametric: the exchangeably weighted bootstrap and the model-based bootstrap (also known as the parametric bootstrap). The funded research promotes the use of semi-nonparametric models in analyzing modern complex data by developing a series of innovative and valid bootstrap inferential tools.

Congratulations, Professor Guang Cheng!

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