Random Variable over probability space

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SUMMARY

The discussion centers on the properties of a random variable \( Y \) defined as \( Y = \min\{1, X\} \), where \( X \) is a random variable over the probability space \( (\Omega, F, P) \). The true statement among the provided options is (3): \( Y \leq X \) for every outcome \( \omega \in \Omega \). The other statements are proven false through counterexamples, particularly statement (1), which can be invalidated by considering specific cases of \( \Omega \) and \( X \).

PREREQUISITES
  • Understanding of random variables and their properties
  • Knowledge of probability spaces, including \( \sigma \)-algebras
  • Familiarity with cumulative distribution functions (CDFs) and expectations
  • Basic concepts of minimum functions in real analysis
NEXT STEPS
  • Study the properties of cumulative distribution functions (CDFs) in detail
  • Explore the concept of expectations in probability theory
  • Learn about the implications of transformations of random variables
  • Investigate counterexamples in probability to strengthen understanding of false statements
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Students and professionals in statistics, mathematicians, and anyone studying probability theory who seeks to deepen their understanding of random variables and their transformations.

Francobati
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Hello. Can you help me solve it? ($F$ is a $\sigma $ algebra).
Let $X$ be a rv over $(\Omega ,F,P)$. Set $Y:= min\left \{ 1,X \right \}$. What statement is TRUE?

(1): $\left \{ Y=X \right \}\neq \Omega $;
(2): $F_Y(x)=F_X(x)$ for every $x\epsilon \Re $;
(3): $Y\leqslant X$ for every outcome;
(4): $E(Y)=\int_{-\infty}^1xd F_{X}(x)$;
(5): $E(Y)=\int_{\Re }max\left \{ 1,x \right \}d F_{X}(x)$.
 
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What are your thoughts on this problem? Can you eliminate any of the answer choices?
 
I have to find the true answer among these five.
 
That's clear, but you haven't mentioned which answer choices you know can be eliminated.
 
I have no idea. What information do I need to resolve it? Help me, please.
 
Ok, let's start with what is the definition of $E[X]$ and $F(x)$? What do these two concepts represent in general for random variables?
 
$E(X)=:x_{1}P(A_{1})+...+x_{k}P(A_{k})\geqslant 0$
$F(x)=P(X\leqslant x)$
 
By the very definition of $Y$, isn't it the case that $Y(\omega) \le X(\omega)$ for all $\omega\in \Omega$?
 
Yes, the answer is 3. But as I explain in an exhaustive way?
 
  • #10
For all pairs of real numbers $a$ and $b$, $\min\{a,b\} \le a$ and $\min\{a,b\} \le b$. So, for all $\omega\in \Omega$, $Y(\omega) \le 1$ and $Y(\omega) \le X(\omega)$. In particular, for every $\omega\in \Omega$, $Y(\omega) \le X(\omega)$.

For the other cases, you can explain why they are false using counterexamples. Take for instance statement (1). If we let $\Omega = [0,1]$ and $X$ the indicator of $[0,1/2]$, then $X\le 1$ and hence $Y$ is identical to $X$; therefore, the event $(Y = X)$ is equal to the whole sample space $\Omega$.
 
  • #11
Ok. Many thanks.
 

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