Expected value of a variable and its reciprocal

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Discussion Overview

The discussion revolves around the relationship between the expected value of a variable and the expected value of its reciprocal, specifically questioning whether a higher expected value of one variable implies a lower expected value of its reciprocal compared to another variable. The scope includes theoretical exploration and mathematical reasoning.

Discussion Character

  • Exploratory
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • Some participants argue that if E(X1) > E(X2), it does not necessarily imply that E(1/X1) < E(1/X2, citing counterexamples involving zero or small values.
  • Others suggest considering cases with positive random variables to explore the implications further.
  • A participant presents a mathematical condition where E(1/X) = 1/E(X) holds only when the two values are equal, indicating a special case.
  • Another participant provides an example with a uniform distribution to illustrate that E(X) can be greater than E(Y) while E(1/X) is also greater than E(1/Y).
  • Some participants discuss the challenge of finding examples where E(X) > E(Y) and E(1/X) > E(1/Y) while also considering the variances of the distributions.
  • A later reply mentions that for positive numbers, a distribution with a small weight near zero can lead to a situation where E(X) > E(Y) and E(1/X) > E(1/Y).

Areas of Agreement / Disagreement

Participants do not reach a consensus. There are multiple competing views on the implications of expected values and their reciprocals, with some arguing against a direct relationship and others exploring specific cases that challenge or support the initial question.

Contextual Notes

Participants note the importance of considering the nature of the random variables involved, particularly whether they can take on zero or negative values, which significantly affects the expected values of their reciprocals.

jeremy22511
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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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