Math Problem Statement

The joint density of X and Y is f(x, y) = (y2 − x2) 8 e−y, 0 < y < ∞, −y x y Show that E[X|Y = y] = 0

Solution

To show that E[XY=y]=0E[X \mid Y = y] = 0, we need to calculate the conditional expectation of XX given Y=yY = y.

Step 1: Determine the Joint Density Function

The joint density function of XX and YY is given by:

f(x,y)=(y2x2)8ey,0<y<,y<x<y.f(x, y) = (y^2 - x^2) \cdot 8 e^{-y}, \quad 0 < y < \infty, \, -y < x < y.

Step 2: Find the Marginal Density of YY

To find the marginal density fY(y)f_Y(y), integrate the joint density over all possible values of XX:

fY(y)=yyf(x,y)dx=yy(y2x2)8eydx.f_Y(y) = \int_{-y}^{y} f(x, y) \, dx = \int_{-y}^{y} (y^2 - x^2) \cdot 8 e^{-y} \, dx.

Simplifying the expression inside the integral:

fY(y)=8eyyy(y2x2)dx.f_Y(y) = 8 e^{-y} \int_{-y}^{y} (y^2 - x^2) \, dx.

Next, let's compute the integral:

yy(y2x2)dx.\int_{-y}^{y} (y^2 - x^2) \, dx.

This is a standard integral that can be solved as:

[ \int_{-y}^{y} (y^2 - x^2) , dx = \left[y^2 x - \frac{x^3}{3}\right]_{-y}^{y} = \left[y^3 - \frac{y^3}{3}\right] - \left[-y^3 + \frac{(-y)^3}{3}\right]. ]

=(y3y33)(y3+y33)=4y33.= \left(y^3 - \frac{y^3}{3}\right) - \left(-y^3 + \frac{y^3}{3}\right) = \frac{4y^3}{3}.

So, the marginal density of YY is:

fY(y)=8ey4y33=32y33ey.f_Y(y) = 8 e^{-y} \cdot \frac{4y^3}{3} = \frac{32 y^3}{3} e^{-y}.

Step 3: Find the Conditional Density of XY=yX \mid Y = y

The conditional density of XX given Y=yY = y is given by:

fXY(xy)=f(x,y)fY(y)=(y2x2)8ey32y33ey.f_{X \mid Y}(x \mid y) = \frac{f(x, y)}{f_Y(y)} = \frac{(y^2 - x^2) \cdot 8 e^{-y}}{\frac{32 y^3}{3} e^{-y}}.

Simplifying:

fXY(xy)=(y2x2)832y33=3(y2x2)4y3.f_{X \mid Y}(x \mid y) = \frac{(y^2 - x^2) \cdot 8}{\frac{32 y^3}{3}} = \frac{3 (y^2 - x^2)}{4 y^3}.

fXY(xy)=3(y2x2)4y3,y<x<y.f_{X \mid Y}(x \mid y) = \frac{3 (y^2 - x^2)}{4 y^3}, \quad -y < x < y.

Step 4: Calculate E[XY=y]E[X \mid Y = y]

To find E[XY=y]E[X \mid Y = y], we use:

E[XY=y]=yyxfXY(xy)dx=yyx3(y2x2)4y3dx.E[X \mid Y = y] = \int_{-y}^{y} x f_{X \mid Y}(x \mid y) \, dx = \int_{-y}^{y} x \frac{3 (y^2 - x^2)}{4 y^3} \, dx.

Simplifying the integral:

E[XY=y]=34y3yyx(y2x2)dx.E[X \mid Y = y] = \frac{3}{4 y^3} \int_{-y}^{y} x (y^2 - x^2) \, dx.

Notice that the integrand is an odd function of xx because it is x×(y2x2)x \times (y^2 - x^2), and we are integrating it over a symmetric interval [y,y][-y, y]. Therefore, the integral of an odd function over a symmetric interval is zero:

yyx(y2x2)dx=0.\int_{-y}^{y} x (y^2 - x^2) \, dx = 0.

Thus:

E[XY=y]=0.E[X \mid Y = y] = 0.

This confirms that:

E[XY=y]=0.E[X \mid Y = y] = 0.

Would you like any further details or have any questions about this proof?

Related Questions:

  1. How do you find the variance of XY=yX \mid Y = y?
  2. What is the significance of the expectation being zero in terms of symmetry?
  3. How can we verify the marginal density of YY is correctly normalized?
  4. What is the joint distribution of XX and YY if it were to represent a physical phenomenon?
  5. How would the results change if the bounds on XX were different?

Tip: When dealing with joint density functions, always ensure that the function is normalized correctly to make valid probability statements!

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Math Problem Analysis

Mathematical Concepts

Probability Theory
Joint Density Function
Conditional Expectation
Symmetry of Functions

Formulas

f(x, y) = (y^2 - x^2) 8 e^{-y}, 0 < y < ∞, −y < x < y
Marginal density: f_Y(y) = ∫_{-y}^{y} f(x, y) dx
Conditional expectation: E[X|Y = y] = ∫_{-y}^{y} x f_{X|Y}(x|y) dx

Theorems

Symmetry Property of Odd Functions
Conditional Expectation

Suitable Grade Level

Undergraduate-Level Probability or Advanced High School Mathematics