Conditional expectation (discrete + continuous)

In summary, the problem asks to find the expected value of X given Y, where X is uniformly distributed over [0,1] and Y is defined as 1 if X>= c and 0 if X < c. The expected value of X given Y=1 is E[X] and given Y=0 is E[X|Y=0]. The best predictor of X based on Y is essentially E[X|Y]. Solving the problem, the expected value of X given Y is (1+c)/2 for Y=1 and c/2 for Y=0. The variance of X is (c^2)/12. Letting Z = X-E[X|Y], the variance of Z can be solved by subtract
  • #1
island-boy
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0
I need help in solving the following problem:

Let X be uniformly distributed over [0,1]. And for some c in (0,1), define Y = 1 if X>= c and Y = 0 if X < c. Find E[X|Y].

My main problem is that I am having difficulty solving for f(X|Y) since X is continuous (uniform continuous over [0,1]) while Y is discrete (takes values of 0 or 1 only)
 
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  • #2
Actually, "E[X|Y]" does not make sense to me. That is the expected value of X given that Y is what? E[X|Y= 1] can be done and E[X|Y= 0].

Of course, if Y= 1, then X>= c so that is just E[X] where X is now uniformly distributed over [c, 1]. If Y= 0, then X is uniformly distributed over [0, c).
 
  • #3
The problem is asking for the best predictor of X based on Y which is essentially E(X|Y)...I believe I should have different answers for different values of Y.

I got what you are saying though HallsofIvy. I'll try to work out the problem again. Thanks!
 
  • #4
Hi, an additional question regarding the problem...

Given the question above, here's what I was able to get (that's suppose to be a chart below):

X: ......[c,1].......[0,c)
Y:.......1.......0
E[X|Y]:......(1+c)/2 ......c/2
X-E[X|Y]:...[(c-1)/2, (1-c)/2]......[-c/2, c/2 )

Solving, I got Var(X) = (c^2)/12

letting Z = X-E[X|Y]

How do I solve for Var(Z)
 
Last edited:

1. What is conditional expectation?

Conditional expectation is a statistical concept that refers to the expected value of a random variable given certain conditions or information. It is denoted as E(X|Y), where X is the random variable and Y is the condition or information.

2. How is conditional expectation calculated for discrete random variables?

For discrete random variables, conditional expectation is calculated by summing the products of each possible value of the random variable and its corresponding conditional probability. This can be expressed mathematically as E(X|Y) = ∑ x P(X=x|Y), where x represents each possible value of X.

3. How is conditional expectation calculated for continuous random variables?

For continuous random variables, conditional expectation is calculated by integrating the product of the random variable and its conditional probability density function over its support. This can be expressed mathematically as E(X|Y) = ∫ x f(x|Y) dx, where f(x|Y) is the conditional probability density function.

4. What is the relationship between conditional expectation and conditional probability?

Conditional expectation and conditional probability are closely related, as they both involve calculating the expected value of a random variable given certain conditions or information. However, while conditional probability is a measure of the likelihood of an event occurring given certain conditions, conditional expectation is a measure of the average value of a random variable given those conditions.

5. How is conditional expectation used in practical applications?

Conditional expectation has various practical applications in fields such as finance, economics, and engineering. It can be used to model and predict outcomes in uncertain situations, make decisions based on incomplete information, and evaluate the performance of statistical models. Additionally, it is a key concept in regression analysis and machine learning algorithms.

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