Calculating Expectation of $X$ for a Nonnegative RV

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  • Thread starter Francobati
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In summary, the correct answer to the given exercise is (2): $E(\frac{1}{\varepsilon }{X})=\frac{7}{\varepsilon }P(X\leqslant 7)+7P(X> 7)$. This can be found by using properties of expectation and the fact that $I_A(w)=1$ if $w\in A$ and $0$ if $w\notin A$.
  • #1
Francobati
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Good morning. Can you help me to solve this exercise. The correct answer should be the 2, but how is it calculated? Thanks.

Let $l_{+}$ be the set of nonnegative simple rv’s. Pick $X=7\cdot I _{\left \{ X\leqslant 7 \right \}}+7\varepsilon \cdot I_{\left \{ X> 7 \right \}}\epsilon l_{+}$ , for $\varepsilon > 0$. What statement is TRUE?

(1): $E(X)=P(X>7)$;
(2): $E(\frac{1}{\varepsilon }{X})=\frac{7}{\varepsilon }P(X\leqslant 7)+7P(X> 7)$;
(3): $E(X)=0$;
(4): $\frac{E(X)}{7}=P(X\geqslant 7)$, provided that $\varepsilon \rightarrow 0$;
(5): $E(X)=\varepsilon $.
 
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  • #2
Hi Francobati,

Welcome. (Wave) Your problem can be solved using the following properties of expectation.

1. $E(X + Y) = E(X) + E(Y)$

2. $E(tX) = tE(X)$ ($t$ is a constant)

Give it a try, and if you have any questions then let me know. :D
 
  • #3
Thanks, but I can not do the calculation because I have not understood the indicator operation.
 
  • #4
If $A$ is an event, then $I_A(w) = 1$ if $w\in A$ and $0$ if $w\notin A$. So $E(I_A) = P(A)$.
 
  • #5
Ok, thank you. In the case of exercise which calculations I have to do to get a reply? Excuse me, but I am stuck.
 
  • #6
Using the addition property (property 1) for expectation, we have

$$E(X) = E(7I_{\{X \le 7\}}) + E(7\varepsilon I_{\{X > 7\}})$$

By property 2 of expectation,

$$E(7I_{\{X \le 7\}}) = 7E(I_{\{X\le 7\}})\quad \text{and}\quad E(7\varepsilon I_{\{X > 7\}}) = 7\varepsilon E(I_{\{X > 7\}}).$$

Thus

$$E(X) = 7E(I_{\{X \le 7\}}) + 7\varepsilon E(I_{\{X > 7\}}).$$

Now use the result $E(I_A) = P(A)$ for any event $A$ to determine which answer choice is best.
 
  • #7
Thanks. As a step from $E(X)=7E(I_{{X\leqslant 7}})+7\varepsilon E(I_{X> 7})$ to $E(\frac{1}{\varepsilon }{X})=\frac{7}{\varepsilon }P(X\leqslant 7)+7P(X> 7)$;? I do some recollection to common factor? And what?
 
  • #8
There are intermediate steps involved. What is $E(I_{X > 7})$? What is $E(I_{X \le 7})$? Answer those questions first.
 
  • #9
Unfortunately I do not know to calculate it. Is $E(I_{X>7})=P(X>7)$ and $E(I_{X\leqslant 7})=P(X\leqslant 7)$?
 
  • #10
You are correct!
 
  • #11
And after?
 
  • #12
Well, look at the last equation in Post #6. Since $E(I_{X \leqslant 7}) = P(X \leqslant 7)$ and $E(I_{X > 7}) = P(X > 7)$, then

$$E(X) = 7P(X \leqslant 7) + 7\varepsilon P(X > 7)$$
 
  • #13
Ok, thanks. Perfect! Now I have to go by $E(X)=7P(X\leqslant 7)+7\varepsilon P(X>7)$ to $E(\frac{1}{\varepsilon}X)=\frac{7}{\varepsilon}P(X\leqslant 7)+7P(X>7)$. What calculations do I need to do? There is perhaps some recollection to common factor to do?
 
  • #14
You correctly identified answer (2) as the correct answer choice. To get the that step, use property (2) of expectation.
 
  • #15
$E(tX)=tE(X)$
$E(\frac{1}{\varepsilon }X)=\frac{1}{\varepsilon }E(X)$
$E(X)=7P(X\leqslant 7)+7\varepsilon P(X>7)$
$E(\frac{1}{\varepsilon }X)=\frac{1}{\varepsilon }(7P(X\leqslant 7)+7\varepsilon P(X>7))$
$E(\frac{1}{\varepsilon }X)=\frac{7}{\varepsilon }P(X\leqslant 7)+7P(X>7)$
Correct?
 
  • #16
Yes, that's correct.
 

1. What is the formula for calculating expectation of a nonnegative random variable?

The formula for calculating expectation of a nonnegative random variable is:
E[X] = ΣxP(X=x), where x is the possible values of the random variable X and P(X=x) is the probability of X taking on that value.

2. Can the expectation of a nonnegative random variable be negative?

No, the expectation of a nonnegative random variable cannot be negative. Since the random variable only takes on nonnegative values, the sum of the probabilities cannot result in a negative value.

3. How is the expectation of a nonnegative random variable calculated if it follows a continuous probability distribution?

If the nonnegative random variable follows a continuous probability distribution, the expectation can be calculated using the integral:
E[X] = ∫ x*f(x) dx, where x is the possible values of the random variable X and f(x) is the probability density function.

4. Is expectation affected by the sample size of the data set?

No, expectation is not affected by the sample size of the data set. The expectation of a random variable is a property of the underlying probability distribution, not the sample size.

5. How can calculating expectation of a nonnegative random variable be useful in real-life scenarios?

Calculating expectation of a nonnegative random variable can be useful in various real-life scenarios, such as in finance to estimate the expected return on an investment, in insurance to calculate the expected cost of claims, and in risk analysis to determine the expected value of a risky event. It can also provide insights into the overall behavior of a random variable and aid in decision-making processes.

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