Unformatted text preview: 1. Start with k = 0 and i = 1. For the model with k ≤ p factors, at step i , the i th observation is removed. 2. The k components are extracted and the regression is ﬁt on the remaining data. 3. The predicted value for the left out point is calculated. 4. Repeat steps 1-3 on all points. 5. The diﬀerence between the sum of squared predicted residuals for the model with k compo-nents and the sum of squared predicted residuals for the minimizing model is calculated. 6. Repeat steps i-v on all models with k ≤ p factors, and 1 ≤ i ≤ n , and choose the model with the smallest number of factors that is signiﬁcantly diﬀerent from the minimizing model. 1...
View Full Document
- Spring '08
- Regression Analysis, cross validation