Scholarly Colloquia and Events

  • 9/22 Statistics Special Lecture Series

     

    Special Lecture Series:

    Dimension Reduction in Regression

    by

    Visiting Prof. Lixing Zhu
    Chair Professor and Department Head
    Department of Mathematics
    Hong Kong Baptist University

     

     

    This lecture series consists of three lectures to introduce high-dimensional semiparametric regression estimation procedures as follows.

     September 22, 2014- Lecture 1 

    Why dimension reduction and how dimension reduction: some basic concepts

     In regression modelling, data visualization plays an important role and some useful tools have been developed such as residual plots when we do not have specific model structure at hand. In low-dimensional paradigms, these tools are very useful to get ideas about underlying regression models. However, in high-dimensional paradigms, residual plots can only get the profiles of the whole picture of underlying models and the information may mislead further modelling. Therefore, if we explore and identify dimension reduction structure first and then the further modelling can be proceeded such that the classical visualization tools can be satisfactorily performed. In this lecture, the sufficient dimension reduction concept, particularly the dimension reduction subspace called the central subspace will be introduced as the preparation for us for dimension reduction estimation in lectures 2 and 3 later.

     TIME AND PLACE

    4:40 – 5:30 p.m.

    Philip E. Austin Building – Room 344

    Coffee will be served at 4:10 in room 326

    For complete lecture series notice visit www.stat.uconn.edu 

     

    For more information, contact: Tracy Burke at tracy.burke@uconn.edu