I Expected value of a variable and its reciprocal

AI Thread Summary
The discussion centers on the relationship between the expected values of a variable and its reciprocal, specifically questioning if E(X1) > E(X2) implies E(1/X1) < E(1/X2). Counterexamples demonstrate that this is not necessarily true, particularly when considering variables that can take on the value of zero. The conversation also explores cases with positive random variables, revealing that E(1/X) can exceed E(1/Y) even when E(X) > E(Y). It highlights that the relationship holds only under specific conditions, particularly when variables are non-negative. Overall, the thread emphasizes the complexity of expected values and their reciprocals in probability theory.
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If E(X1)>E(X2), does it mean that E(1/X1)<E(1/X2)? Thanks!
If E(X1)>E(X2), does it mean that E(1/X1)<E(1/X2)? Thanks!
 
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Hint: Try constant random variables.
 
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No: Example two values X1=0 or 10. X2=1 or 3 (equal prob. for both).

E(X1)=5 and E(X2)=2. E(1/X1) is infinite, E(1/X2)=2/3.
 
mathman said:
No: Example two values X1=0 or 10. X2=1 or 3 (equal prob. for both).

E(X1)=5 and E(X2)=2. E(1/X1) is infinite, E(1/X2)=2/3.

Thanks! What about the case with positive random variables? I probably should have given some context with my question. I was trying to make sense of using the two-tailed region in hypothesis testing by showing that it minimizes the expected value of P(H0 | z-score in critical region). But this value depends on the expected value of the reciprocal of statistical power, which I can show to be maximized by the region. So I was trying to understand whether a higher E(beta) would translate to a lower E(1/beta).
 
jeremy22511 said:
Summary:: If E(X1)>E(X2), does it mean that E(1/X1)<E(1/X2)? Thanks!

If E(X1)>E(X2), does it mean that E(1/X1)<E(1/X2)? Thanks!
Why don't you try some simple examples to see what happens? There seem to a lot of students who are unfamiliar with the concept of looking for a simple counterexample.
 
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mathman said:
No: Example two values X1=0 or 10. X2=1 or 3 (equal prob. for both).

E(X1)=5 and E(X2)=2. E(1/X1) is infinite, E(1/X2)=2/3.
##1/X_1## makes absolutely no sense.
 
Instead of 0, try some arbitrarily small positive number.
 
jeremy22511 said:
What about the case with positive random variables?
Try fixing Y as 1 and letting X take two values, equally likely, with a mean slightly greater than 1.
 
In general, if ##X## takes two values ##a, b## with equal probability, then ##E(\frac 1 X) = \frac 1 {E(X)}## only when ##a = b##:
$$E(\frac 1 X) = \frac 1 2 (\frac 1 a + \frac 1 b) = \frac 1 {ab}(\frac{a + b}{2}) = \frac 1 {ab} E(X)$$
$$E(\frac 1 X) = \frac 1 {E(X)} \ \Rightarrow \ E(X)^2 = ab \ \Rightarrow \ (a+b)^2 = 4ab \ \Rightarrow \ (a-b)^2 = 0$$
 
  • #10
Look at a uniform dist X on (0.01,0.99). E(X) =0.5 and Y a uniform on (0.35,0.45) so E(Y)=0.4

so E(X)>E(Y), but also E(1/X)>E(1/Y) as E(1/Y) =2.5 and E(1/X)=4.7
 
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  • #11
PeroK said:
In general, if ##X## takes two values ##a, b## with equal probability, then ##E(\frac 1 X) = \frac 1 {E(X)}## only when ##a = b##:
$$E(\frac 1 X) = \frac 1 2 (\frac 1 a + \frac 1 b) = \frac 1 {ab}(\frac{a + b}{2}) = \frac 1 {ab} E(X)$$
$$E(\frac 1 X) = \frac 1 {E(X)} \ \Rightarrow \ E(X)^2 = ab \ \Rightarrow \ (a+b)^2 = 4ab \ \Rightarrow \ (a-b)^2 = 0$$
If that's in reply to post #8, I was not suggesting 1/E(X)=E(1/X). The idea is to find a pair of values for X such that E(X)>1 =E(Y) and E(1/X)>1=E(1/Y).
@BWV seems to have come up with a closely related example.
 
  • #12
haruspex said:
If that's in reply to post #8, I was not suggesting 1/E(X)=E(1/X). The idea is to find a pair of values for X such that E(X)>1 =E(Y) and E(1/X)>1=E(1/Y).
@BWV seems to have come up with a closely related example.
It was just a general post to show that one doesn't have to be clever to find an example. The result the OP was looking for can only be true in a special case.
 
  • #13
PeroK said:
It was just a general post to show that one doesn't have to be clever to find an example. The result the OP was looking for can only be true in a special case.
Hmm... ok, but I don't see how your post shows that. How does it give an example of E(X)>E(Y) while E(1/X)>E(1/Y)?
 
  • #14
haruspex said:
Hmm... ok, but I don't see how your post shows that. How does it give an example of E(X)>E(Y) while E(1/X)>E(1/Y)?
I forgot that bit!
 
  • #15
To make amends for post #9. If we let ##a > 1## and consider ##X## equally likely to be ##a## or ##\frac 1 a##, then:
$$E(X) = E(\frac 1 X) = \frac 1 2 (a + \frac 1 a)$$
Hence, as ##a## increases both ##E(X)## and ##E(\frac 1 X)## increase.
 
  • #16
More challenging to find an example where E(X)>E(Y) and E(1/X) >E(1/Y)
where Var(X) < Var(Y).
 
  • #17
BWV said:
More challenging to find an example where E(X)>E(Y) and E(1/X) >E(1/Y)
where Var(X) < Var(Y).
Not really hard. Let X== 1 and Y be uniform on [-2,-1].
 
  • #18
FactChecker said:
Not really hard. Let X== 1 and Y be uniform on [-2,-1].
I think it was sort of agreed earlier in the thread that X and Y should be taken as always nonnegative.
 
  • #19
haruspex said:
I think it was sort of agreed earlier in the thread that X and Y should be taken as always nonnegative.
In that case, I suspect it is impossible. I am not sure how to prove it.
 
  • #20
actually for positive numbers, just need one number close to zero with a small weight in the distribution
for example:
X=[10^-20,1000:2000]
Y=1:1999

E(X)=1499 >E(Y)=1000
Var(X)=8.6*10^4<Var(Y)=3.3*10^5

E(X^-1)=10^16>E(Y^-1)<1
 
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