1

Consider a random variable $V$ defined on the probability space $(\Omega, \mathcal{F}, \mathbb{P})$ such that

1) The support of $V$ is an open subset $\mathcal{V}$ of $\mathbb{R}^K$ with strictly positive Lebesgue measure.

2) The distribution of $V$ is absolutely continuous on $\mathcal{V}$ with respect to Lebsgue measure.

Question: which of the two assumptions is sufficient for having $\forall v\in \mathcal{V}$ $$ \mathbb{P}(V=v)=0 $$ ?


My thoughts: I'm tempted to say that 1) is sufficient for the desired conclusion as 1) implies that the support of $\mathcal{V}$ is non-finite. 2) adds more by implying that the cdf of $V$ is continuous and there is a pdf. Could you say whether I'm right or wrong and why?

Star
  • 414
  • 1
    How do you define support? It seems that the first property is trivial as any measure is supported on $\mathbb{R}^K$. Also is $\Omega=\mathbb{R}^K$? – Yanko May 26 '18 at 14:25
  • 2
    Anyway, as long as I'm aware, $V$ being absolutely continuous with respect to Lebesgue measure means that $P(V=v)\leq \mathcal{L}({v})=0$. – Yanko May 26 '18 at 14:27
  • The support of $V$ is intended as ${v\in \mathbb{R}^K \text{ s.t. } \mathbb{P}({\omega \in \Omega \text{ s.t. } V(\omega) \in B(v,r)})>0 \text{ }\forall r > 0 }$, where $B(v,r)$ denotes the ball with center at $v$ and radius $r$. See here https://math.stackexchange.com/questions/846011/precise-definition-of-the-support-of-a-random-variable
  • – Star May 26 '18 at 14:34
  • $\Omega$ is not $\mathbb{R}^K$. $V$ is defined as a function from $\Omega$ to $\mathbb{R}^K$.
  • – Star May 26 '18 at 14:35
  • Why do you say that the first property is trivial? Suppose that $K=1$ and $V$ is a Bernoulli random variable. Then, I don't think that the support of $V$ is an open subset of $\mathbb{R}$ with strictly positive lebesgue measure. What about the case $K>1$? Thank you for your help
  • – Star May 26 '18 at 14:43