# Modified lognormal power-law distribution

The modified lognormal power-law (MLP) function is a three parameter function that can be used to model data that have characteristics of a log-normal distribution and a power law behavior. It has been used to model the functional form of the initial mass function (IMF). Unlike the other functional forms of the IMF, the MLP is a single function with no joining conditions.

## Functional form

The closed form of the probability density function of the MLP is as follows:
    ${\begin{aligned}f(m)={\frac {\alpha }{2}}\exp \left(\alpha \mu _{0}+{\frac {\alpha ^{2}\sigma _{0}^{2}}{2}}\right)m^{-(1+\alpha )}{\text{erfc}}\left({\frac {1}{\sqrt {2}}}\left(\alpha \sigma _{0}-{\frac {\ln(m)-\mu _{0}}{\sigma _{0}}}\right)\right),\ m\in [0,\infty )\end{aligned}}$
where ${\begin{aligned}\alpha ={\frac {\delta }{\gamma }}\end{aligned}}$ is the asymptotic power-law index of the distribution. Here $\mu _{0}$ and $\sigma _{0}^{2}$ are the mean and variance, respectively, of an underlying lognormal distribution from which the MLP is derived.

## Mathematical properties

Following are the few mathematical properties of the MLP distribution:

### Cumulative distribution

The MLP cumulative distribution function ($F(m)=\int _{-\infty }^{m}f(t)\,dt$) is given by:
    ${\begin{aligned}F(m)={\frac {1}{2}}{\text{erfc}}\left(-{\frac {\ln(m)-\mu _{0}}{{\sqrt {2}}\sigma _{0}}}\right)-{\frac {1}{2}}\exp \left(\alpha \mu _{0}+{\frac {\alpha ^{2}\sigma _{0}^{2}}{2}}\right)m^{-\alpha }{\text{erfc}}\left({\frac {\alpha \sigma _{0}}{\sqrt {2}}}\left(\alpha \sigma _{0}-{\frac {\ln(m)-\mu _{0}}{{\sqrt {2}}\sigma _{0}}}\right)\right)\end{aligned}}$
We can see that as $m\to 0,$ that $\textstyle F(m)\to {\frac {1}{2}}\operatorname {erfc} \left(-{\frac {\ln(m-\mu _{0})}{{\sqrt {2}}\sigma _{0}}}\right),$ which is the cumulative distribution function for a lognormal distribution with parameters μ$_0$ and σ$_0$.

### Mean, variance, raw moments

The expectation value of $M$$^k$ gives the $k$th raw moment of $M$,
    ${\begin{aligned}\langle M^{k}\rangle =\int _{0}^{\infty }m^{k}f(m)\mathrm {d} m\end{aligned}}$
This exists if and only if α > $k$, in which case it becomes:
    ${\begin{aligned}\langle M^{k}\rangle ={\frac {\alpha }{\alpha -k}}\exp \left({\frac {\sigma _{0}^{2}k^{2}}{2}}+\mu _{0}k\right),\ \alpha >k\end{aligned}}$
which is the $k$th raw moment of the lognormal distribution with the parameters μ$_0$ and σ$_0$ scaled by α⁄α-$k$ in the limit α→∞. This gives the mean and variance of the MLP distribution:
    ${\begin{aligned}\langle M\rangle ={\frac {\alpha }{\alpha -1}}\exp \left({\frac {\sigma _{0}^{2}}{2}}+\mu _{0}\right),\ \alpha >1\end{aligned}}$
    ${\begin{aligned}\langle M^{2}\rangle ={\frac {\alpha }{\alpha -2}}\exp \left(2\left(\sigma _{0}^{2}+\mu _{0}\right)\right),\ \alpha >2\end{aligned}}$
Var($M$) = ⟨$M$$^2$⟩-(⟨$M$⟩)$^2$ = α exp(σ$_0$$^2$ + 2μ$_0$) (⁠exp(σ$_0$$^2$)/α-2⁠ - ⁠α/(α-2)$^2$⁠), α > 2

### Mode

The solution to the equation $f'(m)$ = 0 (equating the slope to zero at the point of maxima) for $m$ gives the mode of the MLP distribution.
    $f'(m)=0\Leftrightarrow K\operatorname {erfc} (u)=\exp(-u^{2}),$
where $\textstyle u={\frac {1}{\sqrt {2}}}\left(\alpha \sigma _{0}-{\frac {\ln m-\mu _{0}}{\sigma _{0}}}\right)$ and $K=\sigma _{0}(\alpha +1){\tfrac {\sqrt {\pi }}{2}}.$
Numerical methods are required to solve this transcendental equation. However, noting that if $K$≈1 then u = 0 gives us the mode $m$$^*$:
    $m^{*}=\exp(\mu _{0}+\alpha \sigma _{0}^{2})$

### Random variate

The lognormal random variate is:
    ${\begin{aligned}L(\mu ,\sigma )=\exp(\mu +\sigma N(0,1))\end{aligned}}$
where $N(0,1)$ is standard normal random variate. The exponential random variate is :
    ${\begin{aligned}E(\delta )=-\delta ^{-1}\ln(R(0,1))\end{aligned}}$
where R(0,1) is the uniform random variate in the interval [0,1]. Using these two, we can derive the random variate for the MLP distribution to be:
    ${\begin{aligned}M(\mu _{0},\sigma _{0},\alpha )=\exp(\mu _{0}+\sigma _{0}N(0,1)-\alpha ^{-1}\ln(R(0,1)))\end{aligned}}$