In probability theory and statistics, the U-quadratic distribution is a continuous probability distribution defined by a unique convex quadratic function with lower limit a and upper limit b.

$f(x|a,b,\alpha ,\beta )=\alpha \left(x-\beta \right)^{2},\quad {\text{for }}x\in [a,b].$ Parameters Probability density function $a:~a\in (-\infty ,\infty )$ $b:~b\in (a,\infty )$ or$\alpha :~\alpha \in (0,\infty )$ $\beta :~\beta \in (-\infty ,\infty ),$ $x\in [a,b]\!$ $\alpha \left(x-\beta \right)^{2}$ ${\alpha \over 3}\left((x-\beta )^{3}+(\beta -a)^{3}\right)$ ${a+b \over 2}$ ${a+b \over 2}$ $a{\text{ and }}b$ ${3 \over 20}(b-a)^{2}$ $0$ ${3 \over 112}(b-a)^{4}$ TBD See text See text

## Parameter relations

This distribution has effectively only two parameters a, b, as the other two are explicit functions of the support defined by the former two parameters:

$\beta ={b+a \over 2}$ (gravitational balance center, offset), and

$\alpha ={12 \over \left(b-a\right)^{3}}$ (vertical scale).

One can introduce a vertically inverted ($\cap$ )-quadratic distribution in analogous fashion.

## Applications

This distribution is a useful model for symmetric bimodal processes. Other continuous distributions allow more flexibility, in terms of relaxing the symmetry and the quadratic shape of the density function, which are enforced in the U-quadratic distribution – e.g., beta distribution and gamma distribution.

## Moment generating function

$M_{X}(t)={-3\left(e^{at}(4+(a^{2}+2a(-2+b)+b^{2})t)-e^{bt}(4+(-4b+(a+b)^{2})t)\right) \over (a-b)^{3}t^{2}}$ how do you get this??

## Characteristic function

$\phi _{X}(t)={3i\left(e^{iate^{ibt}}(4i-(-4b+(a+b)^{2})t)\right) \over (a-b)^{3}t^{2}}$ 