Stochastic ordering

The following rules describe cases when one random variable is stochastically less than or equal to another. Strict version of some of these rules also exist.

1. ${\displaystyle A\preceq B}$ if and only if for all non-decreasing functions ${\displaystyle u}$, ${\displaystyle {\rm {E}}[u(A)]\leq {\rm {E}}[u(B)]}$.
2. If ${\displaystyle u}$ is non-decreasing and ${\displaystyle A\preceq B}$ then ${\displaystyle u(A)\preceq u(B)}$
3. If ${\displaystyle u:\mathbb {R} ^{n}\to \mathbb {R} }$ is an increasing function and ${\displaystyle A_{i}}$ and ${\displaystyle B_{i}}$ are independent sets of random variables with ${\displaystyle A_{i}\preceq B_{i}}$ for each ${\displaystyle i}$, then ${\displaystyle u(A_{1},\dots ,A_{n})\preceq u(B_{1},\dots ,B_{n})}$ and in particular ${\displaystyle \sum _{i=1}^{n}A_{i}\preceq \sum _{i=1}^{n}B_{i}}$ Moreover, the ${\displaystyle i}$th order statistics satisfy ${\displaystyle A_{(i)}\preceq B_{(i)}}$.
4. If two sequences of random variables ${\displaystyle A_{i}}$ and ${\displaystyle B_{i}}$, with ${\displaystyle A_{i}\preceq B_{i}}$ for all ${\displaystyle i}$ each converge in distribution, then their limits satisfy ${\displaystyle A\preceq B}$.
5. If ${\displaystyle A}$, ${\displaystyle B}$ and ${\displaystyle C}$ are random variables such that ${\displaystyle \sum _{c}\Pr(C=c)=1}$ and ${\displaystyle \Pr(A>u|C=c)\leq \Pr(B>u|C=c)}$ for all ${\displaystyle u}$ and ${\displaystyle c}$ such that ${\displaystyle \Pr(C=c)>0}$, then ${\displaystyle A\preceq B}$.

The hazard rate of a non-negative random variable ${\displaystyle X}$ with absolutely continuous distribution function ${\displaystyle F}$ and density function ${\displaystyle f}$ is defined as

${\displaystyle r(t)={\frac {d}{dt}}(-\log(1-F(t)))={\frac {f(t)}{1-F(t)}}.}$

Given two non-negative variables ${\displaystyle X}$ and ${\displaystyle Y}$ with absolutely continuous distribution ${\displaystyle F}$ and ${\displaystyle G}$, and with hazard rate functions ${\displaystyle r}$ and ${\displaystyle q}$, respectively, ${\displaystyle X}$ is said to be smaller than ${\displaystyle Y}$ in the hazard rate order (denoted as ${\displaystyle X\leq _{hr}Y}$) if

${\displaystyle r(t)\geq q(t)}$ for all ${\displaystyle t\geq 0}$,

or equivalently if

${\displaystyle {\frac {1-F(t)}{1-G(t)}}}$ is decreasing in ${\displaystyle t}$.

Let ${\displaystyle X}$ and ${\displaystyle Y}$ two continuous (or discrete) random variables with densities (or discrete densities) ${\displaystyle f\left(t\right)}$ and ${\displaystyle g\left(t\right)}$, respectively, so that ${\displaystyle {\frac {g\left(t\right)}{f\left(t\right)}}}$ increases in ${\displaystyle t}$ over the union of the supports of ${\displaystyle X}$ and ${\displaystyle Y}$; in this case, ${\displaystyle X}$ is smaller than ${\displaystyle Y}$ in the likelihood ratio order (${\displaystyle X\leq _{lr}Y}$).

If two variables have the same mean, they can still be compared by how "spread out" their distributions are. This is captured to a limited extent by the variance, but more fully by a range of stochastic orders.

Convex order is a special kind of variability order. Under the convex ordering, ${\displaystyle A}$ is less than ${\displaystyle B}$ if and only if for all convex ${\displaystyle u}$, ${\displaystyle {\rm {E}}[u(A)]\leq {\rm {E}}[u(B)]}$.

Laplace transform order compares both size and variability of two random variables. Similar to convex order, Laplace transform order is established by comparing the expectation of a function of the random variable where the function is from a special class: ${\displaystyle u(x)=-\exp(-\alpha x)}$. This makes the Laplace transform order an integral stochastic order with the generator set given by the function set defined above with ${\displaystyle \alpha }$ a positive real number.

Considering a family of probability distributions ${\displaystyle ({P}_{\alpha })_{\alpha \in F}}$ on partially ordered space ${\displaystyle (E,\preceq )}$ indexed with ${\displaystyle \alpha \in F}$ (where ${\displaystyle (F,\preceq )}$ is another partially ordered space, the concept of complete or realizable monotonicity may be defined. It means, there exists a family of random variables ${\displaystyle (X_{\alpha })_{\alpha }}$ on the same probability space, such that the distribution of ${\displaystyle X_{\alpha }}$ is ${\displaystyle {P}_{\alpha }}$ and ${\displaystyle X_{\alpha }\preceq X_{\beta }}$ almost surely whenever ${\displaystyle \alpha \preceq \beta }$. It means the existence of a monotone coupling. See[3]