Can a Counterexample Disprove a Conditional Expected Value Statement?

In summary, a counterexample has been provided to show that the statement "If $ X (\omega) = \varphi(Y (\omega))$ for all $\omega\in A $ , then $\mathbb{E}(X|Y )(\omega) = \varphi(Y (\omega))$ for almost all $\omega\in A $." can be false if $A$ is not a $\sigma_Y$ measurable set. This counterexample involves a random variable $Y$ and measurable function $\varphi$ where $A$ is not in the $\sigma_Y$ sigma-algebra, showing that $\mathbb{E}(X|Y )(\omega) \neq \varphi(Y (\omega
  • #1
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I know that the following is true and I've already proven it.

Let $Y$ be a random variable and $\varphi$ a measurable function.

Let $A$ be a $\Sigma_Y$ measurable set.

If $ X (\omega) = \varphi(Y (\omega))$ for all $\omega\in A $ , then $\mathbb{E}(X|Y )(\omega) = \varphi(Y (\omega))$ for almost all $\omega\in A $.

I cannot, however, come up with an counterexample which proves that the above statement can be false if $A \not\in \Sigma_Y$.

Could you help me with that?

That is, I'm looking for a random variable $Y$ sych that for $A \not\in \sigma(Y)$ , $\omega \in A$ $$ X (\omega) = \varphi(Y (\omega))$$ but $$\mathbb{E}(X|Y )(\omega) \neq \varphi(Y (\omega)).$$
 
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  • #2
A counterexample could be the following. Let $Y$ be a uniform random variable on $[0,1]$ and $\varphi(x) = x^2$. Let $A = \{ \omega \in [0,1] : \omega \not\in \mathbb{Q} \}$ so that $A \not\in \sigma(Y)$. Then for $\omega \in A$, $X(\omega) = \varphi(Y(\omega)) = Y(\omega)^2$. However, $\mathbb{E}(X|Y )(\omega) = \frac{1}{2}Y(\omega)^2 \neq \varphi(Y (\omega))$.
 

FAQ: Can a Counterexample Disprove a Conditional Expected Value Statement?

What is conditional expected value?

Conditional expected value is a statistical concept that measures the average outcome of a random variable, given that certain conditions are met. In other words, it is the expected value of a variable under a specific condition or set of conditions.

How is conditional expected value calculated?

The formula for calculating conditional expected value is similar to that of regular expected value, but it takes into account the specific conditions. It can be calculated by taking the sum of all possible outcomes multiplied by their respective probabilities, given the conditions, and dividing by the number of outcomes.

What is the difference between conditional expected value and regular expected value?

The main difference between conditional expected value and regular expected value is that conditional expected value takes into account a specific condition or set of conditions, while regular expected value considers all possible outcomes equally. Additionally, conditional expected value is often used in situations where there is a known relationship between variables.

When is conditional expected value used in research?

Conditional expected value is often used in research when there is a need to understand the average outcome of a variable under certain conditions. It can be useful in predicting outcomes and understanding the relationship between variables. It is also commonly used in decision making and risk analysis.

Can conditional expected value be negative?

Yes, conditional expected value can be negative. This means that the average outcome of a variable, given the conditions, is below zero. This can happen when there is a high probability of negative outcomes or when the conditions have a negative impact on the variable. It is important to consider the sign of the conditional expected value when interpreting the results.

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