Random Variable over probability space

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Discussion Overview

The discussion revolves around a problem involving a random variable \(X\) defined over a probability space \((\Omega, F, P)\) and a new random variable \(Y\) defined as \(Y = \min\{1, X\}\). Participants are exploring the truth of several statements regarding the properties of \(Y\) in relation to \(X\), including aspects of expectation and distribution functions.

Discussion Character

  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • Some participants propose that statement (3), which asserts \(Y \leq X\) for every outcome, is true based on the definition of \(Y\).
  • Others argue that statement (1) may not be true in all cases, providing a counterexample where \(Y\) is identical to \(X\).
  • A participant seeks clarification on the definitions of expectation \(E[X]\) and cumulative distribution function \(F(x)\) in the context of random variables.
  • There is uncertainty regarding which of the five statements can be eliminated as incorrect.
  • Some participants express a need for further information to resolve the problem and clarify the statements.

Areas of Agreement / Disagreement

Participants generally agree that statement (3) is true, but there is disagreement regarding the validity of other statements, particularly (1). The discussion remains unresolved as to which statements can be definitively eliminated.

Contextual Notes

Participants have not reached a consensus on the truth of all statements, and some statements are challenged with counterexamples. The discussion reflects varying interpretations of the properties of random variables and their expectations.

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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