Index: The Book of Statistical ProofsGeneral Theorems ▷ Probability theory ▷ Probability functions ▷ Inverse transformation method

Theorem: Let $U$ be a continuous random variable having a standard uniform distribution. Then, the random variable

$\label{eq:cdf-itm} X = F_X^{-1}(U)$

has a probability distribution characterized by the invertible cumulative distribution function $F_X(x)$.

Proof: The cumulative distribution function of the transformation $X = F_X^{-1}(U)$ can be derived as

$\label{eq:cdf-itm-qed} \begin{split} &\hphantom{=} \;\; \mathrm{Pr}(X \leq x) \\ &= \mathrm{Pr}(F_X^{-1}(U) \leq x) \\ &= \mathrm{Pr}(U \leq F_X(x)) \\ &= F_X(x) \; , \end{split}$

because the cumulative distribution function of the standard uniform distribution $\mathcal{U}(0,1)$ is

$\label{eq:suni-cdf} U \sim \mathcal{U}(0,1) \quad \Rightarrow \quad F_U(u) = \mathrm{Pr}(U \leq u) = u \; .$
Sources:

Metadata: ID: P221 | shortcut: cdf-itm | author: JoramSoch | date: 2021-04-07, 08:47.