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Mar 17

## Distribution and density functions of a linear transformation ####  Distribution and density functions of a linear transformation - two short derivations to read during breakfast.

### Distribution function of a linear transformation

Let $X$ be a random variable and let $Y=aX+b$ be its linear transformation (here $a,b$ are some real numbers and $a\ne0$, otherwise $Y$ is not random). If the distribution function $F_X$ is known, what will be the distribution function of $Y$?

The answer is obtained in one line if you know the definition of the distribution function:

(1) $F_Y(y)=P(Y\le y)=P(aX+b\le y)=P(X\le\frac{y-b}{a})=F_X(\frac{y-b}{a})$.

For the inequalities $aX+b\le y$ and $X\le\frac{y-b}{a}$ to be equivalent, we have to assume that $a>0$ (for applications this is enough). The case $a<0$ is left as an exercise.

### Density function of a linear transformation

As above, $Y$ is a linear transformation of $X$. Suppose they have densities $p_X,p_Y$. What is the relationship between the densities?

Recall formula (1) that links distribution and density functions. Equation (1) in terms of densities becomes $\int_{-\infty}^yp_Y(t)dt=\int_{-\infty}^{\frac{y-b}{a}}p_X(t)dt$.

Let's differentiate this equation. The Newton-Leibnitz formula applied to the integral on the left gives $p_Y$ evaluated at $y$. On the right, additionally, we have to use the chain rule. The result is

(2) $p_Y(y)=p_X(\frac{y-b}{a})\frac{d}{dy}\frac{y-b}{a}=\frac{1}{a}p_X(\frac{y-b}{a})$.

Equation (2) will be used to derive ML estimators for the linear model.