Problem 2: We've discussed estimating Xn+h (h 2 1) from (X1,. X,), but we can apply similar tech- niques to estimate missing values. In this problem, let (X,) be a MA(1) process, X, = Z+0Z1-1 with Z, ~ WN(0, o²). Let Xy denote the best (in terms of mean squared error) linear estimate of X3 using the data (X1, X2, X4, Xs); see section 2.5.2 in the book for details. a) Find an expression for X b) Using the values o? = 1/3 and 0= v2, what is the mean squared error of the estimate?

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Author:Amos Gilat
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Problem 2: We've discussed estimating Xn+h (h 2 1) from (X1,...X,), but we can apply similar tech-
niques to estimate missing values. In this problem, let (X,) be a MA(1) process, X, = Z+0Z1-1,
with Z ~ WN(0, o²). Let X3 denote the best (in terms of mean squared error) linear estimate
of X3 using the data (X1, X2, X4, X;); see section 2.5.2 in the book for details.
a) Find an expression for X3
b) Using the values o? = 1/3 and 0 == v2, what is the mean squared error of the estimate?
Transcribed Image Text:Problem 2: We've discussed estimating Xn+h (h 2 1) from (X1,...X,), but we can apply similar tech- niques to estimate missing values. In this problem, let (X,) be a MA(1) process, X, = Z+0Z1-1, with Z ~ WN(0, o²). Let X3 denote the best (in terms of mean squared error) linear estimate of X3 using the data (X1, X2, X4, X;); see section 2.5.2 in the book for details. a) Find an expression for X3 b) Using the values o? = 1/3 and 0 == v2, what is the mean squared error of the estimate?
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