Difference between revisions of "Expected value"

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Let $\mathbb{T}$ be a [[time scale]]. Let $X$ be a random variable with [[probability density function]] $f \colon \mathbb{T} \rightarrow \mathbb{R}$. The expected value of $X$ is given by
 
Let $\mathbb{T}$ be a [[time scale]]. Let $X$ be a random variable with [[probability density function]] $f \colon \mathbb{T} \rightarrow \mathbb{R}$. The expected value of $X$ is given by
$$\mathrm{E}_{\mathbb{T}}(X) = \dfrac{dC_f}{dz}(0),$$
+
$$\mathrm{E}_{\mathbb{T}}(X^k)=\displaystyle\int_{-\infty}^{\infty} k! h_k(t,0)f(t) \Delta t.$$
where $C_f$ is the [[cumulant generating function]] of $f$.
 
  
 
=Properties=
 
=Properties=

Revision as of 05:17, 26 April 2015

Let $\mathbb{T}$ be a time scale. Let $X$ be a random variable with probability density function $f \colon \mathbb{T} \rightarrow \mathbb{R}$. The expected value of $X$ is given by $$\mathrm{E}_{\mathbb{T}}(X^k)=\displaystyle\int_{-\infty}^{\infty} k! h_k(t,0)f(t) \Delta t.$$

Properties

Theorem: The following formula holds: $$\mathrm{E}_{\mathbb{T}}(X^k)=\displaystyle\int_0^{\infty} k! h_k(t,0)f(t) \Delta t.$$

Proof:

Example

Theorem

Let $X$ have the uniform distribution on $[a,b] \cap \mathbb{T}$. Then, $$\mathrm{E}_{\mathbb{T}}(X) = \dfrac{h_2(\sigma(b),a)}{\sigma(b)-a}+a,$$ where $h_2$ denotes the delta hk and $\sigma$ denotes the forward jump.

Proof

References

Theorem

If $X$ is a random variable with the exponential distribution with parameter $\lambda$, then $$\mathrm{E}_{\mathbb{T}}(X)=\dfrac{1}{\lambda}.$$

Proof

References

Theorem: Let $X$ have the gamma distribution on $\mathbb{T}$. Then $$\mathrm{E}_{\mathbb{T}}(X)=\dfrac{k}{\lambda}.$$

Proof:

References

Probability theory on time scales and applications to finance and inequalities by Thomas Matthews