I know there are many related questions to this but none are exactly to the point I want to ask , My question is in terms of simple linear regression,
This statement(In bold)from the book An Introduction to Statistical Learning
In Leave-One-Out Cross validation the statistical learning method is fit on the n − 1 training observations, and a prediction is made for the excluded observation, using its value x1. Since (x1, y1) was not used in the fitting process, MSE1 = provides an approximately unbiased estimate for the test error. But even though MSE1 is unbiased for the test error, it is a poor estimate because it is highly variable, since it is based upon a single observation (x1, y1).
So my questions are.
How can I tell whether any method/model provides an unbiased estimate for the TEST ERROR?(I know well about unbiased estimates for parameters).
How does the above apply to this specific case of Leave-One-Out Cross validation ?