INFERENCE AFTER VARIABLE SELECTION

This thesis presents inference for the multiple linear regression model Y = beta_1 x_1 + ... + beta_p x_p + e after model or variable selection, including prediction intervals for a future value of the response variable Y_f, and testing hypotheses with the bootstrap. If n is the sample size, most results are for n/p large, but prediction intervals are developed that may increase in average length slowly as p increases for fixed n if the model is sparse: k predictors have nonzero coefficients beta_i where n/k is large.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Campus Only Dissertation

Discipline thesis:degree_discipline

Mathematics

Year

2017

Author dc:creator

• Pelawa Watagoda, Lasanthi Chathurika Ranasinghe

Contributors dc:contributor

• Olive, David

• Bootstrap
• Forward Selection
• Lasso
• Prediction Interval
• Relaxed Lasso
• Ridge Regression

Repository record dc:identifier

https://opensiuc.lib.siu.edu/dissertations/1424

OAI identifier oai:identifier

oai:opensiuc.lib.siu.edu:dissertations-2428