HW 3 Solutions
March 18, 2013
Grade distribution: Problems 1 - 5: 12 points each, Problem 6: 15 points
for writeup, 15 points for computation.
Problem 1
a) Let cfw_X, y denote the full original datase
Statistics 315a
Midterm Exam
4:15-5:30pm, February 29, 2012.
You may use the class text, notes and calculators, but not computers or any
device that connects to the Internet. The questions below requi
Statistics 315a
Homework 3, due Wednesday March 12, 2014.
1. ESL 5.7.
2. Let X be the n p regression matrix for a logistic regression, with
p
n, and y the response vector. Assume X has row-rank n. Let
Problem Set 1 Solutions
Statistics 315A
Problem 1
(a)
0.05
Both the k -nearest neighbor (KNN) and linear regression classiers are implemented in the code
included in the appendix. The performance of t
Problem Set 3 Solutions
Statistics 315A
Problem 1
1(a) Since it is known that column rank(X ) = row rank(X ) = N , the columns of X span RN . By
denition, it is therefore always possible to nd a satis
STATS 315A
Winter 2007
Homework 1
Solutions
Prob. #1 (Thanks to Wei Zhen) (a) The function mixG takes a centroid matrix mu, a vector N specifying the number of samples in each group and the noise vari
Statistics 315a
Homework 1, due Monday Jan 25, 2010.
1. Compare the classication performance of linear regression and k
nearest neighbor classication on the zipcode data. In particular, consider only
Statistics 315a
Homework 1, due Wednesday January 29, 2014.
ESL refers to the course textbook, and ESL 2.4 refers to exercise 2.4 in
ESL. Since the homework assignments count 70% of your nal grade, yo
Statistics 315a
Homework 2, due Wednesday February 13, 2013.
1. ESL 3.12 & 3.30
2. ESL 3.15
3.
(a) Suppose that we run a ridge regression with parameter on a
single variable X , and get coecient a. We
Statistics 315a
Homework 2, due Wednesday February 12, 2014.
1. ESL 3.12 & 3.30
2.
(a) Suppose that we run a ridge regression with parameter on a
single variable X , and get coecient a. We now include
Stats 315A HW2 Solutions
8th February, 2012
If there are any questions regarding the solutions or the grades of HW 2, please contact
Max ([email protected]) with Stats315A-hw2-grading in the subject l
Statistics 315a
Homework 3, due Wednesday March 13, 2013.
1. ESL 7.3. Part (c) is somewhat open ended. Any reasonable suggestions
will be acceptable.
2. With yi = f (xi )+ i , and var(yi |xi ) = 2 , d
Statistics 315a
Homework 1, due Wednesday January 30, 2013.
ESL refers to the course textbook, and ESL 2.4 refers to exercise 2.4 in
ESL. Since the homework assignments count 70% of your nal grade, yo
Stats 315A HW2 Solutions
February 17, 2014
If there are any questions regarding the solutions or the grades of HW 2, please contact
Austen ([email protected]) with Stats315A-hw2-grading in the subjec
Statistics 315a
Homework 2, due Wednesday February 12, 2014.
1. ESL 3.12 & 3.30
2.
(a) Suppose that we run a ridge regression with parameter on a
single variable X , and get coecient a. We now include
STAT 315A Homework 2 Solutions
Problem 1: ESL 4.2
a) For LDA,
k = x 1 k 1/2k 1k + log (Nk /N ).
Substitute this formula into the requirement that 2 (x) 1 (x) > 0 and we get the
solution
x 1 (2 1 ) +
Generalized additive models
General supervised learning (regression) problem. N observations of
an outcome variable Y and predictors X1 , X2 , , . . . Xp .
Goal is to predict outcome from predictors
Stats 315A HW1 Solutions
6th February, 2012
If there are any questions regarding the solutions or the grades of HW 1, please contact
Gourab ([email protected]) with Stats315A-hw1-grading in the subj
STAT 315a
Midterm winter 2013- Solutions
Duration- 1 hour and 15 min
Aids allowed: class text, class notes, and calculators
The questions below require fairly short answers and are of equal value. The
Statistics 315a
Homework 1, due Wednesday Oct 15 , 2008.
1. Compare the classication performance of linear regression and k
nearest neighbor classication on the zipcode data. In particular, consider
SS3859: Fall 2011
Solution Manual Homework 2
Chen Yang
1. Solution:
(a) The conditional probability density function for yi given xi is
1
e
fYi |Xi (yi |xi ) =
2
(yi 0 1 xi )2
2 2
.
(b) The joint pro
Problem Set 3 Solutions
Statistics 315A
Problem 1
1(a) Since it is known that column rank(X ) = row rank(X ) = N , the columns of X span RN . By
denition, it is therefore always possible to nd a satis
ESL Chapter 3 Linear Methods for Regression
Trevor Hastie and Rob Tibshirani
Linear Methods for Regression
Outline
The simple linear regression model
Multiple linear regression
Model selection and
Statistics 315a
Homework 2, due Monday, Feb 8, 2010.
1. ESL 4.2
2. Lasso and LAR: Consider the lasso problem in Lagrange multiplier
form: with L( ) = i (yi j xij j )2 , we minimize
L( ) +
|j |
(1)
j
1
STAT 315a
Midterm Winter 2009
Duration- 1 hour and 15 min
Aids allowed: class text, class notes, and calculators
The questions below require fairly short answers and are of equal value.
They are in