Due Thursday, Jan. 21

Homework #3

1. Write a Matlab function M-file to find the least squares parameter estimates for an AR process. The inputs to the M-file should be the order of the process and the y(t) sequence. The outputs of the M-file should be the AR parameter estimates.

2. Write a Matlab function M-file to implement the recursive least squares algorithm to estimate the parameters of an AR process.  The inputs to the M-file should be the order of the process and the y(t) sequence. The outputs of the M-file should be the AR parameter estimates at each time point.

3. Test your programs using data obtained by a simulation of the following process.  Test the effect of changing the length of the data sequence on the accuracy of your parameter estimates.  Compare the LS and RLS estimates.

y(t) + 1.5y(t-1) + 0.7y(t-2) = e(t)  (variance of e(t) is 1)

   From textbook

    4.    3-2, 3-6, 3-7, 3-11

    5.    5-2, 5-4

Due Thursday, February 18

Homework #4

   From textbook

    6-8, 6-10, 6-11, 6-12, 6-13, 6-15, 6-16

Due Thursday, Feb. 25

This Homework will not be collected. The first exam will take place at this lecture.

Homework #5

   From textbook

7-1, 7-2, 7-4, 8-3, 8-10

Due Thursday, March 10

Homework #6

   From textbook

 9-2, 9-3, 9-6, 9-7, 11-5, 11-8, 11-11, 12-8, 12-9, 13-6, 13-9

Due Thursday, March 31

   From textbook

    14-4, 14.4, 14-6, 14-7, 15-5, 15-6, 15.9

Kalman Simulation (This will be collected.)

 Due April 14

The second exam will take place at this lecture.