Variance

Let $\mathbb{T}$ be a time scale. Let $X$ be a random variable with probability density function $f \colon \mathbb{T} \rightarrow \mathbb{R}$. Then the variance of $X$ is defined by the formula $$\mathrm{Var}_{\mathbb{T}}(X) = \dfrac{d^2 C_f}{dz^2}(0).$$

Properties

Theorem: The following formula holds: $$\mathrm{Var}_{\mathbb{T}}(X) = \mathrm{E}_{\mathbb{T}}(X^2) - (\mathrm{E}_{\mathbb{T}}(X))^2.$$

Proof: █

Examples

Proposition: Let $X$ have the uniform distribution on $[a,b] \cap \mathbb{T}$. Then, $$\mathrm{Var}_{\mathbb{T}}(X)=2\dfrac{h_3(\sigma(b),0)-h_3(a,0)}{\sigma(b)-a}-\left( \dfrac{h_2(\sigma(b),a)}{\sigma(b)-a}+a \right)^2.$$

Proof: █

Theorem

If $X$ with a random variable with the exponential distribution with parameter $\lambda$, then, $$\mathrm{Var}_{\mathbb{T}}(X)=\dfrac{1}{\lambda^2},$$ where $\mathrm{Var}$ denotes variance.

References

Theorem: Let $X$ have the gamma distribution on $\mathbb{T}$. Then $$\mathrm{Var}_{\mathbb{T}}(X)=\dfrac{k}{\lambda^2}.$$

Proof: █

References

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