Expectation: Is this proposition true or false?

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The discussion centers on the proposition regarding the limit of x[1 - F(x)] as x approaches infinity for a continuous random variable X with an existing expectation E(X). Participants are exploring various mathematical approaches, including l'Hopital's rule and integration techniques, to either prove or find a counterexample to this proposition. A hint is provided that relates to the expectation of X and its behavior as a function of a threshold variable, \bar x. The conversation highlights the understanding that proving the case for nonnegative X can extend to the entire real line. Overall, the participants are collaboratively working towards clarifying the limit's behavior in relation to the cumulative distribution function (CDF) of X.
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If X is a continuous random variable and E(X) exists, does the limit as x→∞ of x[1 - F(x)] = 0?

I encountered this, but so far I have neither been able to prove this, nor find a counterexample. I have tried the mathematical definition of the limit, l'Hopital's rule, integration by parts, a double integral (through expectation), and various proof scribbles, but so far, nothing has worked. Can anyone help me with this?

EDIT: In this case, the function F is the CDF of X.
 
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A hint, for the case where X\geq 0:

\mathbb E X = \int_0^\infty xf(x)dx \geq \int_0^{\bar x} xf(x)dx + \bar x\int_{\bar x}^\infty f(x)dx.

Think about the pieces of that, and think about limits as \bar x \to \infty.
 
I'm aware of that, and I know how to prove it for the case of X \geq 0, but I'm confused about the case of the entire real line.

Also, what do you mean by \overline{x}?
 
I was just using \bar x as another stand-in variable.

If you know how to prove it for nonnegative-valued X, then you're basically done. The limiting property you care about is the same for X_+ = \text{max}\{X,0\}.
 
What do you mean by limiting property?
 
Oh! I get it now! Thank you so much!
 
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