25
Dec 21

## Analysis of problems with conditioning

These problems are among the most difficult. It's important to work out a general approach to such problems. All references are to J. Abdey,  Advanced statistics: distribution theory, ST2133, University of London, 2021.

### General scheme

Step 1. Conditioning is usually suggested by the problem statement: $Y$ is conditioned on $X$.

Your life will be easier if you follow the notation used in the guide: use $p$ for probability mass functions (discrete variables) and $f$ for (probability) density functions (continuous variables).

a) If $Y|X$ and $X$ both are discrete (Example 5.1, Example 5.13, Example 5.18): $p_{Y}\left( y\right) =\sum_{Set}p_{Y\vert X}\left( y\vert x\right) p_{X}\left( x\right) .$

b) If $Y|X$ and $X$ both are continuous (Activity 5.6): $f_{Y}\left( y\right) =\int_{Set}f_{Y\vert X}\left( y\vert x\right) f_{X}\left( x\right) dx.$

c) If $Y|X$ is discrete, $X$ is continuous (Example 5.2, Activity 5.5): $p_{Y}\left( y\right) =\int_{Set}p_{Y\vert X}\left( y\vert x\right) f_{X}\left( x\right) dx$

d) If $Y|X$ is continuous, $X$ is discrete (Activity 5.12): $f_{Y}\left( y\right) =\sum_{Set}f_{Y\vert X}\left( y\vert x\right) p_{X}\left( x\right) .$

In all cases you need to figure out $Set$ over which to sum or integrate.

Step 2. Write out the conditional densities/probabilities with the same arguments
as in your conditional equation.

Step 3. Reduce the result to one of known distributions using the completeness
axiom.

### Example 5.1

Let $X$ denote the number of hurricanes which form in a given year, and let $Y$ denote the number of these which make landfall. Suppose each hurricane has a probability of $\pi$ making landfall independent of other hurricanes. Given the number of hurricanes $x$, then $Y$ can be thought of as the number of successes in $x$ independent and identically distributed Bernoulli trials. We can write this as $Y|X=x\sim Bin(x,\pi )$. Suppose we also have that $X\sim Pois(\lambda )$. Find the distribution of $Y$ (noting that $X\geq Y$ ).

### Solution

Step 1. The number of hurricanes $X$ takes values $0,1,2,...$ and is distributed as Poisson. The number of landfalls for a given $X=x$ is binomial with values $y=0,...,x$. It follows that $Set=\{x:x\ge y\}$.

Write the general formula for conditional probability: $p_{Y}\left( y\right) =\sum_{x=y}^{\infty }p_{Y\vert X}\left( y\vert x\right) p_{X}\left( x\right) .$

Step 2. Specifying the distributions: $p_{X}\left( x\right) =e^{-\mu }\frac{\mu ^{x}}{x!},$ where $x=0,1,2,...,$

and $P\left( Bin\left( x,\pi \right) =y\right) =p_{Y\vert X}\left( y\vert x\right) =C_{x}^{y}\pi ^{y}\left( 1-\pi \right) ^{x-y}$ where $y\leq x.$

Step 3. Reduce the result to one of known distributions: $p_{Y}\left( y\right) =\sum_{x=y}^{\infty }C_{x}^{y}\pi ^{y}\left( 1-\pi \right) ^{x-y}e^{-\mu }\frac{\mu ^{x}}{x!}$

(pull out of summation everything that does not depend on summation variable $x$) $=\frac{e^{-\mu }\mu ^{y}}{y!}\pi ^{y}\sum_{x=y}^{\infty }\frac{1}{\left( x-y\right) !}\left( \mu \left( 1-\pi \right) \right) ^{x-y}$

(replace $x-y=z$ to better see the structure) $=\frac{e^{-\mu }\mu ^{y}}{y!}\pi ^{y}\sum_{z=0}^{\infty }\frac{1}{z!}\left( \mu \left( 1-\pi \right) \right) ^{z}$

(using the completeness axiom $\sum_{x=0}^{\infty }\frac{\mu ^{x}}{x!}=e^{\mu }$ for the Poisson variable) $=\frac{e^{-\mu }}{y!}\left( \mu \pi \right) ^{y}e^{\mu \left( 1-\pi \right) }=\frac{e^{-\mu \pi }}{y!}\left( \mu \pi \right) ^{y}=p_{Pois(\mu \pi )}\left( y\right) .$