If I have two continuous distributions $f(x)$ and $g(x)$, there are several mathematical ways to combine $f$ and $g$ to get new distributions. Which correspond to what statistical interpretation? For example, if I multiply $f$ and $g$ into $fg$, does $fg$ have a statistical meaning? What about $f/g$, $f circ g$ and $f star g$ (convolution)? Also, can we do the same with discrete distributions?
Best Answer
If $f$ and $g$ represent the densities of independent random variables, then the product $f_X(x)g_Y(y)$ is the density of $(x,y)$ under the joint distribution of $X$ and $Y$. Similarly, for two discrete distributions, the product of their mass functions is the mass function of their joint distribution.
As demonstrated here, convolving the two derives the density or mass of their sum, the random variable $Z = X + Y$. (Note that this only holds when $X$ and $Y$ are independent.)
I suppose one could view $frac{f(x)}{g(x)}$ as the Bayes factor of two candidate models, both with static parameters, after a single observation $x$. That strikes me as of trivial use, but I'm not aware of another interpretation. (Note though that, as Glen points out below, this cannot be interpreted as the density of some other random variable.)
Similar Posts:
- Solved – How to add two dependent random variables
- Solved – Does Wolfram Mathworld make a mistake describing a discrete probability distribution with a probability density function
- Solved – Does Bayes theorem apply to joint distributions of discrete and continuous random variables
- Solved – Definition of independence of two random vectors and how to show it in the jointly normal case
- Solved – Let X and Y be independent random variables and suppose Y is symmetric(around 0). Show that XY is symmetric