Expectation Inequality for Positive Random Variables

In summary, the conversation discusses proving that the expectation of a random variable X is greater than a positive constant a times the probability of X being greater than a. Various approaches are mentioned, including using the exponential distribution and the normal distribution. The problem statement is questioned due to the ambiguity of the inequality symbol used.
  • #1
theperthvan
184
0

Homework Statement


Prove that
E(X) > a.P(X>a)


Homework Equations


E(X) is expectation, a is a positive constant and X is the random variable.
(Note, > should be 'greater than or equal to' but I'm not too sure how to do it)


The Attempt at a Solution


Well I can show it easy enough for X~Exp(h), but of course this is not a general proof.
And I played around a bit with P(X>a)=1-F(a) where F is the cdf, but yeah. That's all I could really do. I just don't really get how the a factor comes in.
 
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  • #2
Take P(x) to be a normal distribution centered at 0. The E(x)=0. Yet a.P(x>a) is clearly positive for a>0. There's something wrong with the problem statement. '>=' is fine for greater than or equal to.
 
  • #3
Dick said:
Take P(x) to be a normal distribution centered at 0. The E(x)=0. Yet a.P(x>a) is clearly positive for a>0. There's something wrong with the problem statement. '>=' is fine for greater than or equal to.

Sorry, X is non-negative.
 
  • #4
Ok then. Define E(X) as an integral and split the integral into the ranges 0-a and a-infinity. Drop the first integral and think about approximating the second by something smaller.
 

FAQ: Expectation Inequality for Positive Random Variables

What is expectation probability proof?

Expectation probability proof is a mathematical concept used to calculate the expected value of a random variable. It involves determining the probability of each possible outcome and multiplying it by the corresponding value. This allows us to estimate the average outcome of a random variable over multiple trials.

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In science, expectation probability proof is commonly used in statistical analysis and data interpretation. It allows scientists to make predictions and draw conclusions about the likelihood of certain outcomes based on a set of data.

What are the key components of an expectation probability proof?

The key components of an expectation probability proof include the random variable, the probability distribution, and the formula for calculating the expected value. The random variable represents the possible outcomes, the probability distribution assigns probabilities to each outcome, and the formula calculates the expected value.

Can expectation probability proof be used for any type of data?

Yes, expectation probability proof can be used for any type of data as long as it follows a probability distribution. This includes both discrete and continuous data.

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