Variable selection in nonparametric additive models

author: Joel Horowitz, Northwestern University
published: Dec. 18, 2008,   recorded: December 2008,   views: 4650
Categories

Slides

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

We consider a nonparametric additive model of a conditional mean function in which the number of variables and additive components may be much larger than the sample size but the number of non-zero additive compo- nents is small relative to the sample size. The statistical problem is to determine which additive components are non-zero. The additive compo- nents are approximated by truncated series expansions with B-spline bases. The adaptive group LASSO is used to select non-zero components. We give conditions under which this procedure selects the non-zero components correctly with probability approaching one as the sample size increases. Fol- lowing model selection, oracle-efficient, asymptotically normal estimators of the non-zero components can be obtained by using existing methods. The results of Monte Carlo experiments show that the adaptive group LASSO procedure works well with samples of moderate size.

See Also:

Download slides icon Download slides: sip08_horowitz_vsina_01.pdf (139.4 KB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: