This preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: Answer Sketch for The Elements of Statistical Learning (second edition) YaoLiang Yu University of Alberta [email protected] March 11, 2009 Abstract This partial solution collection to the book The Elements of Statistical Learning (second edition) is a byproduct of my own reading. However, one should NEVER expect the solutions in this collection to be (completely) correct or appropriate or exhaustive (tailored by my own taste). I tried my best when solving these exercises but I cannot give any guarantee (and I am not responsible for any consequence). Mind your own risk if you would like to refer to this collection. Whenever you are sure that there is a mistake in this collection or you have a better solution, please feel free to send me an email and I will be more than happy to revise as much as I can. Chapter 3 Linear Regression Ex. 3.3b (GaussMarkov Theorem) A linear unbiased estimator can be written as: ˜ β = Ay , and the least square estimator is ˆ β = ( X T X ) 1 X T y = X † y . For unbiasedness, we have E ˜ β = A E y...
View
Full
Document
This note was uploaded on 02/29/2012 for the course STATS 315A taught by Professor Tibshirani,r during the Winter '10 term at Stanford.
 Winter '10
 TIBSHIRANI,R
 Statistics

Click to edit the document details