Conditional expectation on an indicator

In summary: Rather than "thinking it is possible" you need to actually prove it. But, at least you are on the right track---that is, your thinking is OK, and just needs to be firmed up. It is not difficult once you see what needs to be established.
  • #1
IniquiTrance
190
0

Homework Statement



Let [itex]X[/itex] and [itex]Y[/itex] be independent Bernoulli RV's with parameter [itex]p[/itex]. Find,
[tex]\mathbb{E}[X\vert 1_{\{X+Y=0\}}][/tex] and [tex]\mathbb{E}[Y\vert 1_{\{X+Y=0\}}][/tex]

Homework Equations

The Attempt at a Solution



I'm trying to show that,

[tex]\mathbb{E}[X+Y\vert 1_{\{X+Y=0\}}] = 0[/tex]

by,

[tex]
\begin{align*}
\mathbb{E}[X+Y\vert 1_{\{X+Y=0\}}] &= \frac{\mathbb{E}[(X+Y)1_{\{X+Y=0\}}]}{\mathbb{P}[X+Y=0]} \\
&= \frac{0}{(1-p)^2} \\
&= 0
\end{align*}
[/tex]
 
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  • #2
IniquiTrance said:
[tex]\mathbb{E}[X\vert 1_{\{X+Y=0\}}][/tex]
I'm not familiar with the notation for the condition ([itex] 1_{\{X+Y=0\}}[/itex]). How does one read it? Can you post a link?
 
  • #3
haruspex said:
I'm not familiar with the notation for the condition ([itex] 1_{\{X+Y=0\}}[/itex]). How does one read it? Can you post a link?

It's the indicator of the event [itex]\{X+Y=0\}[/itex].

[tex]
1_{\{X+Y=0\}}=\begin{cases}1, \qquad \text{if }X+Y=0; \\ 0, \qquad \text{otherwise.}\end{cases}
[/tex]
 
  • #4
IniquiTrance said:

Homework Statement



Let [itex]X[/itex] and [itex]Y[/itex] be independent Bernoulli RV's with parameter [itex]p[/itex]. Find,
[tex]\mathbb{E}[X\vert 1_{\{X+Y=0\}}][/tex] and [tex]\mathbb{E}[Y\vert 1_{\{X+Y=0\}}][/tex]

Homework Equations

The Attempt at a Solution



I'm trying to show that,

[tex]\mathbb{E}[X+Y\vert 1_{\{X+Y=0\}}] = 0[/tex]

by,

[tex]
\begin{align*}
\mathbb{E}[X+Y\vert 1_{\{X+Y=0\}}] &= \frac{\mathbb{E}[(X+Y)1_{\{X+Y=0\}}]}{\mathbb{P}[X+Y=0]} \\
&= \frac{0}{(1-p)^2} \\
&= 0
\end{align*}
[/tex]

Basically, you are trying to show that the conditional distribution ##f(k) \equiv P(X= k|X+Y=0), \; k=0,1,2, \ldots## has mean zero; that is, that ##\sum_{k=0}^{\infty} k f(k) = 0##. How do you suppose that could happen?
 
  • #5
Ray Vickson said:
Basically, you are trying to show that the conditional distribution ##f(k) \equiv P(X= k|X+Y=0), \; k=0,1,2, \ldots## has mean zero; that is, that ##\sum_{k=0}^{\infty} k f(k) = 0##. How do you suppose that could happen?

I think it is possible since both ##X## and ##Y## are Bernoulli, if their sum is 0, then ##\mathbb{P}[X=0|X+Y=0]=1##. Then, ##\sum_{k=0}^1kf(k)=0*1##.
 
  • #6
IniquiTrance said:
I think it is possible since both ##X## and ##Y## are Bernoulli, if their sum is 0, then ##\mathbb{P}[X=0|X+Y=0]=1##. Then, ##\sum_{k=0}^1kf(k)=0*1##.

Rather than "thinking it is possible" you need to actually prove it. But, at least you are on the right track---that is, your thinking is OK, and just needs to be firmed up. It is not difficult once you see what needs to be established.
 

1. What is conditional expectation on an indicator?

Conditional expectation on an indicator is a mathematical concept used in probability theory to calculate the expected value of a random variable conditional on the outcome of another random variable. It is also known as the conditional expected value or the conditional mean.

2. How is conditional expectation on an indicator calculated?

Conditional expectation on an indicator is calculated by taking the expected value of a random variable, given that another random variable has a specific value. This can be represented by the formula E[X|Y=y], where X is the random variable and Y is the indicator variable.

3. What is the significance of conditional expectation on an indicator?

Conditional expectation on an indicator is important in understanding the relationship between two random variables and how one affects the other. It can also be used in making predictions and decisions based on known information.

4. Can conditional expectation on an indicator be negative?

Yes, conditional expectation on an indicator can be negative. This means that the expected value of a random variable, given a specific outcome of another random variable, can be less than zero. This can occur when the two variables have a negative correlation.

5. How is conditional expectation on an indicator used in real life?

Conditional expectation on an indicator is used in various fields such as finance, economics, and engineering to make predictions and decisions based on known information. For example, it can be used in financial modeling to calculate the expected return on an investment given certain market conditions.

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