Semiparametric Learning
A Summer School of Mathematics for Data Science at the Department of Mathematics
When: 26-30 July 2021
Where: University of Trento, Povo (Trento), Italy
In case of impediments due to the COVID-19 pandemic, the school will run completely from remote on the same dates.
Outline
Semiparametric models are ubiquitous in modern statistical application and semiparametrics analysis based on a geometric approach is a very powerful tool to develop valid estimation procedures. We will introduce the semiparametric methods in its general abstract form, and derive its detailed analytic forms in various applications including simple mean estimation, and more complex measurement error models, dimension reduction problems, missing data/causal inference problems. Students will gain a comprehensive understanding of the semiparametric models, methods, theory and applications.
Lecturer
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Bio
Yanyuan Ma is Professor of Statistics at the Department of Statistics at the Pennsylvania State University since 2016. After graduating in mathematics from the Beijing University in 1994, she was a PhD student in Mathematics at Stanford University from 1994-1995, then a PhD student at the Massachusetts Institute of Technology from 1995-1999. After her PhD, she worked in industry for three years, before returning to academia as a postdoctoral researcher in 2002. She became an assistant professor in Statistics at Texas A&M university in 2004, and became a professor in 2006 at the University of Neuchatel. She returned to Texas A&M University in 2008 as an associate professor, and was a professor there in 2011. She then moved to the University of South Carolina in 2014 and then moved to the Pennsylvania State university in 2016. Her main research activities are on semiparametrics and learning, including developing semiparametric methods and efficiency theory for measurement error models, dimension reduction, survival problems, case-control data and other data with selection bias. On these topics, she has co-authored a number of papers published in international journals and her research activities are supported by various funding agencies.Schedule
- 26 July 2021
- 09.00-10.45 and 11.15-13.00: Motivations and Example Semiparametric Models
- 27 July 2021
- 09.00-10.45 and 11.15-13.00: Semiparametric Ideas in Parametric Problems
- 28 July 2021
- 09.00-10.45 and 11.15-13.00: Semiparametrics, RAL estimators, Influence Functions
- 14.30-16:00: Practical session
- 29 July 2021
- 09.00-10.45 and 11.15-13.00: Analysis of Semiparametric Models, Type I
- 14.30-16:00: Practical session
- 30 July 2021
- 09.00-10.45 and 11.15-13.00: Analysis of Semiparametric Models, Type II
All morning lectures will be delivered in two slots per day. Coffee break will be served during the pause and buffet lunch will be available after the end of the morning lectures. After each practical session a refreshment will be available.
References
- Classnotes will be provided in class.
- Efficient and Adaptive Estimation for Semiparametric Models, P. J. Bickel, C. A.J. Klaassen, Y. Ritov, and J. A. Wellner (1998)
- Unified Methods for Censored Longitudinal Data and Causality, M. J. van der Laan and J. M. Robins (2003)
- Semiparametric Theory and Missing Data, A.A. Tsiatis (2006)
- Introduction to Empirical Processes and Semiparametric Inference, M.R. Kosorok (2008)
Registration
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The ideal participant of this school is a PhD or a Master student with background in Mathematics, Probability, Statistics or Data Science. However application is open to everyone.
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There are no school fees. Coffee breaks and lunches would be provided for free to all participants in person.
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In-person attendance is limited to 30 people. Registration is compulsory. Limited resources are available to support the local expenses of some of the participating students. To register (and possibly apply for support) follow this link: you will be asked some information about yourself and standard documentation (CV) and a motivation letter if you apply for financial support. To receive full consideration please submit your application no later than 10 July 2021.
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Online participation requires registration as well. If you plan on attending online, send an email to the school’s address: datascience.maths@unitn.it.
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For further information, please contact Prof. Claudio Agostinelli