Definition:Convergence in Distribution
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Definition
Let $\struct {\Omega, \Sigma, \Pr}$ be a probability space.
Let $\sequence {X_n}_{n \mathop \in \N}$ be a sequence of real-valued random variables on $\struct {\Omega, \Sigma, \Pr}$.
Let $X$ be a real-valued random variable on $\struct {\Omega, \Sigma, \Pr}$.
For each $n \in \N$, let $F_n$ be the cumulative distribution function of $X_n$.
Let $F$ be the cumulative distribution function of $X$.
We say that $\sequence {X_n}_{n \mathop \in \N}$ converges in distribution to $X$ if and only if:
- $\ds \lim_{n \mathop \to \infty} \map {F_n} x = \map F x$
for all $x$ for which $F$ is continuous.
This is written:
- $X_n \xrightarrow d X$
Sources
- 2011: Morris H. DeGroot and Mark J. Schervish: Probability and Statistics (4th ed.): $6.3$: The Central Limit Theorem: Definition $6.3.1$