Transformation of pmf; bivariate to single-variate

In summary, the conversation discusses the difficulty in obtaining a binomial distribution for Y = X_1 - X_2 + n_2 with parameters n = n_1 + n_2 and p = 1/2. Different approaches were suggested, such as using a joint distribution or the moment generating function, but ultimately the Chu-Vandermonde Identity was used to obtain the correct formula for f(y).
  • #1
rayge
25
0
Transformations always give me trouble, but this one does in particular.

Assume [itex]X_1[/itex], [itex]X_2[/itex] independent with binomial distributions of parameters [itex]n_1[/itex], [itex]n_2[/itex], and [itex]p=1/2[/itex] for each.

Show [itex]Y = X_1 - X_2 + n_2[/itex] has a binomial distribution with parameters [itex]n= n_1 + n_2[/itex], [itex]p = 1/2[/itex].

My first instinct was to pick a variable [itex]Z = X_2[/itex], define a joint distribution of [itex]Y[/itex] and [itex]Z[/itex], and sum over all values of [itex]Z[/itex]. I ran into some complex algebra when summing this joint distribution over all values of [itex]Z[/itex], [itex]0[/itex] to [itex]n_2[/itex]. If anyone knows how to sum over all values of z for (n_1 choose y+z-n_2)*(n_2 chooze z) so as to get (n_1 + n_2 choose y), I would love to hear how, but I'm pretty sure this is a no-go.

My next thought was to still choose [itex]Z = X_2[/itex], but this time get the mgf of Y and Z. This boils down to [itex](1/2 + exp(t_1)/2)^{n_1}(1/2 + exp(t_2)/2)^{n_2}[/itex]. When I set [itex]t = t_1 + t_2[/itex], I get an mgf which fits what we're looking for, i.e. the binomial distribution with parameters [itex]n_1[/itex], [itex]n_2[/itex], and [itex]p=1/2[/itex]. But I don't know if that is valid algebra, as a means of obtaining an mgf for a univariate distribution from an mgf for a bivariate distribution.

Any thoughts welcome!
 
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  • #2
Let Z = n2 - X2. Z is binomial - same parameters as X2. This should be easier. (X1 + Z).
 
  • #3
Thanks for the suggestion. From this I get:
[tex]f(y)=\sum_{z=0}^{n_2} \binom{n_1}{y-z}\binom{n_2}{n_2-z}\Big(\frac{1}{2}\Big)^{n_1+n_2}[/tex]
What I want eventually is this:
[tex]f(y)=\binom{n_1+n_2}{y}\Big(\frac{1}{2}\Big)^{n_1+n_2}[/tex]
I want very much to snap my fingers and call these equal, but I don't see it. Expanding the sum I get (apologies if I messed this up):
[tex]\Big(1/2\Big)^{n_1+n_2}\Big(\frac{{n_1!}{n_2!}}{{y!}{(n_1-y)!}{n_2!}{0!}}+\frac{{n_1!}{n_2!}}{{(y-1)!}{(n_1-y+1)!}{(n_2-1)!}{1!}}+\frac{{n_1!}{n_2!}}{{(y-2)!}{(n_1-y+2)!}{(n_2-2)!}{2!}}+\cdots+\frac{{n_1!}{n_2!}}{{(y-n_2)!}{(n_1-y+n_2)!}{n_2!}}\Big)[/tex]
The expansion of what I'm looking for looks like this:
[tex]\Big(\frac{1}{2}\Big)^{n_1+n_2}\frac{{(n_1+n_2)!}}{{y!}{(n_1+n_2-y)!}}[/tex]
Is there some kind of algebra magic I'm missing to get these to equal each other?

Or maybe you were suggesting using this for the mgf approach? Still not sure about getting [itex]t_1=t_2[/itex] (sorry for the typo before).
 
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  • #4
I believe the correct formula for f(y) should be:

[tex]f(y)=\sum_{z=0}^{n_2} \binom{n_1}{y-z}\binom{n_2}{z}\Big(\frac{1}{2}\Big)^{n_1+n_2}[/tex]
 
  • #5
Indeed! Anyone curious about how to get the answer from this, check out the Chu-Vandermonde Identity.

(I ended up using the moment generating function E(e^x_1t-tx_2+tn_2), which was easier than the transformation I had been doing.)
 

FAQ: Transformation of pmf; bivariate to single-variate

1. What is the transformation of pmf from bivariate to single-variate?

The transformation of pmf (probability mass function) from bivariate to single-variate refers to the process of converting a probability distribution with two variables into a probability distribution with only one variable. This is often done to simplify calculations and analysis.

2. Why is it necessary to transform pmf from bivariate to single-variate?

Transforming pmf from bivariate to single-variate is necessary in many statistical analyses, as it allows for a simpler and more intuitive understanding of the data. It also helps to reduce the complexity of calculations and makes it easier to identify patterns and relationships between variables.

3. How is the transformation of pmf from bivariate to single-variate performed?

The transformation of pmf from bivariate to single-variate can be done using various statistical techniques, such as marginalization or integration. This involves integrating or summing over one of the variables in the bivariate distribution to obtain a single-variable distribution.

4. What are the advantages of transforming pmf from bivariate to single-variate?

There are several advantages to transforming pmf from bivariate to single-variate. It simplifies the analysis and calculations, helps to identify patterns and relationships between variables, and makes it easier to interpret the results. It also allows for a more efficient use of data and resources.

5. Are there any limitations to transforming pmf from bivariate to single-variate?

While transforming pmf from bivariate to single-variate can be useful in many cases, it may not always be appropriate or necessary. In some situations, it may lead to the loss of important information or oversimplification of the data. It is important to carefully consider the purpose and context of the analysis before deciding to transform the pmf.

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