hwsol3 - EE 278 Monday, July 27, 2009 Statistical Signal...

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Unformatted text preview: EE 278 Monday, July 27, 2009 Statistical Signal Processing Handout #7 Homework #3 Solutions 1. (20 points) Schwarz inequality. a. Consider the quadratic equation in the parameter a : 0 = E (( X + aY ) 2 ) = E ( X 2 ) + 2 aE ( XY ) + a 2 E ( Y 2 ) . Since it is the expected value of a nonnegative random variable, E (( X + aY ) 2 ) ≥ 0 . If E (( X + aY ) 2 ) > 0 there are two imaginary solutions, while if E (( X + aY ) 2 ) = 0 there is one real solution. Thus the discriminant must satisfy 4( E ( XY )) 2- 4 E ( X 2 ) E ( Y 2 ) ≤ , which we can rewrite as E (( XY )) 2 ≤ E ( X 2 ) E ( Y 2 ) . Here is another proof. Let a = ± s E ( X 2 ) E ( Y 2 ) . Plugging these value into E (( X + aY ) 2 ) ≥ 0, we obtain- E ( XY ) ≤ p E ( X 2 ) E ( Y 2 ) and E ( XY ) ≤ p E ( X 2 ) E ( Y 2 ) Combining the two inequalities for E ( XY ) yields | E ( XY ) | ≤ p E ( X 2 ) E ( Y 2 ) ⇒ E 2 ( XY ) ≤ E ( X 2 ) E ( Y 2 ) which is what we set out to prove. b. If X = cY then E (( cY · Y )) 2 = c 2 ( E ( Y 2 )) 2 = E (( cY ) 2 ) E ( Y 2 ) = E ( X 2 ) E ( Y 2 ) . Conversely, if E 2 ( XY ) = E ( X 2 ) E ( Y 2 ) then the discriminant of the quadratic equation in part (a) is 0. Therefore E (( X + aY ) 2 ) = 0, which means that X + aY = 0 with probability 1. Hence X =- aY with probability 1. Clearly, a = ± s E ( X 2 ) E ( Y 2 ) . c. The square of correlation coefficient is by definition ρ 2 X,Y = Cov 2 ( X, Y ) Var( X )Var( Y ) . If we define random variables U = X- E ( X ) and V = Y- E ( Y ), then ρ 2 X,Y = ( E ( UV )) 2 E ( U 2 ) E ( V 2 ) ≤ 1 , where the inequality follows from the Schwarz Inequality. Therefore | ρ X,Y | ≤ 1 . d. By the Schwarz Inequality, E 2 ( XY ) ≤ E ( X 2 ) E ( Y 2 ) ⇒ E ( XY ) ≤ p E ( X 2 ) E ( Y 2 ) . Therefore E (( X + Y ) 2 ) = E ( X 2 ) + 2 E ( XY ) + E ( Y 2 ) ≤ E ( X 2 ) + 2 p E ( X 2 ) p E ( Y 2 ) + E ( Y 2 ) = p E ( X 2 ) + p E ( Y 2 ) 2 . 2. (20 points) Jensen Inequality. a. For two x points with nonzero p X ( x ), the proof follows by the definition of convexity. Now, suppose that the proof holds for n x points with nonzero p X ( x ). To prove that it holds for n + 1 points, we need to show that g n +1 X i =1 p X ( x i ) x i ! ≤ n +1 X i =1 p X ( x i ) g ( x i ) . Define n X i =1 p X ( x i ) = λ, so p X ( x n +1 ) = 1- λ. Then, n +1 X i =1 p X ( x i ) g ( x i ) = (1- λ ) g ( x n +1 ) + λ n X i =1 p X ( x i ) λ g ( x i ) ≥ (1- λ ) g ( x n +1 ) + λg n X i =1 p X ( x i ) λ x i ! ≥ g (1- λ ) x n +1 + λ n X i =1 p X ( x i ) λ x i ! = g n +1 X i =1 p X ( x i ) x i ! . where the first inequality follows from the induction hypothesis and the second follows from the definition of convexity. b. Once a function g ( X ) has been shown to be convex, applying Jensen’s inequality E ( g ( X )) ≥ g ( E ( X )) gives the inequality relationship....
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This note was uploaded on 10/26/2011 for the course ECE 180 taught by Professor Eggers during the Spring '11 term at Aarhus Universitet.

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hwsol3 - EE 278 Monday, July 27, 2009 Statistical Signal...

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