The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Abstract Details
|
Activity Number:
|
223
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Monday, August 1, 2011 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
Section on Statistical Learning and Data Mining
|
| Abstract - #300767 |
|
Title:
|
Probability Machines
|
|
Author(s):
|
James D. Malley*+
|
|
Companies:
|
National Institutes of Health
|
|
Address:
|
CIT, Bldg. 12A, Rm. 2052, Bethesda, MD, 20892,
|
|
Keywords:
|
classification ;
pattern recognition ;
learning machines
|
|
Abstract:
|
Many statistical learning machines can provide an optimal classification for binary outcomes. However, probabilities are required for risk estimation using individual patient characteristics for personalized medicine. This talk shows that any statistical learning machine that is consistent for the nonparametric regression problem is also consistent for the probability estimation problem. These will be called probability machines.
Probability machines discussed include classification and regression random forests and two nearest-neighbor machines, all of which use any collection of predictors with arbitrary statistical structure. Two simulated and two real data sets with binary outcomes illustrate the use of these machines for probability estimation for an individual.
|
The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
Back to the full JSM 2011 program
|
2011 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.