Index: The Book of Statistical ProofsGeneral Theorems ▷ Probability theory ▷ Expected value ▷ Linearity

Theorem: The expected value is a linear operator, i.e.

$\label{eq:mean-lin} \begin{split} \mathrm{E}(X + Y) &= \mathrm{E}(X) + \mathrm{E}(Y) \\ \mathrm{E}(a\,X) &= a\,\mathrm{E}(X) \end{split}$

for random variables $X$ and $Y$ and a constant $a$.

Proof:

1) If $X$ and $Y$ are discrete random variables, the expected value is

$\label{eq:mean-disc} \mathrm{E}(X) = \sum_{x \in \mathcal{X}} x \cdot f_X(x)$

and the law of marginal probability states that

$\label{eq:lmp-disc} p(x) = \sum_{y \in \mathcal{Y}} p(x,y) \; .$

Applying this, we have

$\label{eq:mean-lin-s1-disc} \begin{split} \mathrm{E}(X + Y) &= \sum_{x \in \mathcal{X}} \sum_{y \in \mathcal{Y}} (x+y) \cdot f_{X,Y}(x,y) \\ &= \sum_{x \in \mathcal{X}} \sum_{y \in \mathcal{Y}} x \cdot f_{X,Y}(x,y) + \sum_{x \in \mathcal{X}} \sum_{y \in \mathcal{Y}} y \cdot f_{X,Y}(x,y) \\ &= \sum_{x \in \mathcal{X}} x \sum_{y \in \mathcal{Y}} f_{X,Y}(x,y) + \sum_{y \in \mathcal{Y}} y \sum_{x \in \mathcal{X}} f_{X,Y}(x,y) \\ &\overset{\eqref{eq:lmp-disc}}{=} \sum_{x \in \mathcal{X}} x \cdot f_X(x) + \sum_{y \in \mathcal{Y}} y \cdot f_{Y}(y) \\ &\overset{\eqref{eq:mean-disc}}{=} \mathrm{E}(X) + \mathrm{E}(Y) \end{split}$

as well as

$\label{eq:mean-lin-s2-disc} \begin{split} \mathrm{E}(a\,X) &= \sum_{x \in \mathcal{X}} a \, x \cdot f_X(x) \\ &= a \, \sum_{x \in \mathcal{X}} x \cdot f_X(x) \\ &\overset{\eqref{eq:mean-disc}}{=} a \, \mathrm{E}(X) \; . \end{split}$

2) If $X$ and $Y$ are continuous random variables, the expected value is

$\label{eq:mean-cont} \mathrm{E}(X) = \int_{\mathcal{X}} x \cdot f_X(x) \, \mathrm{d}x$

and the law of marginal probability states that

$\label{eq:lmp-cont} p(x) = \int_{\mathcal{Y}} p(x,y) \, \mathrm{d}y \; .$

Applying this, we have

$\label{eq:mean-lin-s1-cont} \begin{split} \mathrm{E}(X + Y) &= \int_{\mathcal{X}} \int_{\mathcal{Y}} (x+y) \cdot f_{X,Y}(x,y) \, \mathrm{d}y \, \mathrm{d}x \\ &= \int_{\mathcal{X}} \int_{\mathcal{Y}} x \cdot f_{X,Y}(x,y) \, \mathrm{d}y \, \mathrm{d}x + \int_{\mathcal{X}} \int_{\mathcal{Y}} y \cdot f_{X,Y}(x,y) \, \mathrm{d}y \, \mathrm{d}x \\ &= \int_{\mathcal{X}} x \int_{\mathcal{Y}} f_{X,Y}(x,y) \, \mathrm{d}y \, \mathrm{d}x + \int_{\mathcal{Y}} y \int_{\mathcal{X}} f_{X,Y}(x,y) \, \mathrm{d}x \, \mathrm{d}y \\ &\overset{\eqref{eq:lmp-cont}}{=} \int_{\mathcal{X}} x \cdot f_X(x) \, \mathrm{d}x + \int_{\mathcal{Y}} y \cdot f_Y(y) \, \mathrm{d}y \\ &\overset{\eqref{eq:mean-cont}}{=} \mathrm{E}(X) + \mathrm{E}(Y) \end{split}$

as well as

$\label{eq:mean-lin-s2-cont} \begin{split} \mathrm{E}(a\,X) &= \int_{\mathcal{X}} a \, x \cdot f_X(x) \, \mathrm{d}x \\ &= a \int_{\mathcal{X}} x \cdot f_X(x) \, \mathrm{d}x \\ &\overset{\eqref{eq:mean-cont}}{=} a \, \mathrm{E}(X) \; . \end{split}$

Collectively, this shows that both requirements for linearity are fulfilled for the expected value, for discrete as well as for continuous random variables.

Sources:

Metadata: ID: P53 | shortcut: mean-lin | author: JoramSoch | date: 2020-02-13, 21:08.