Conditional Multinomial Problem

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In summary, the conditional distribution of Y1 given Y3=m (m<n) is a binomial with ( (n-m), p1/(p1+p2) ), where p1+p2+p3=1, y1+y2+y3=n, and y3=m.
  • #1
FaradayLaws
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If Y1,Y2,Y2 ~ Multinomial with parameter (n,p1,p2,p3)
Prove that the conditional distribution of Y1 given Y3=m (m<n)
is a binomial with ( (n-m), p1/(p1+p2) )

p1+p2+p3=1
y1+y2+y3=n
y3=m

My Attempt:
P( Y1=y1| y3=m) = P(Y1=y1, Y3=m)/ P(Y3=m)

( n choose m and y1 ) p1^y1*p3^m / ( n choose m) p3^m*(p1+p2)^n-m

leaving me with
(n-m)!/ y1! (p1/ p1+p2)^y1((p1+p2) ^y2))
...
can't seem to simplyfy this to become a binomial
honestly stuck here!

Thanks!
 
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  • #2
(n choose m)(n-m choose y1) p1^y1*p2^(n-m-y1)*p3^m
divided by
(n choose m) p3^m*(p1+p2)^(n-m)

simplifies to

(n-m choose y1)p1^y1 p2^(n-m-y1) (p1 + p2)^(m - n). Rearrange to obtain the result.
 
Last edited:

Related to Conditional Multinomial Problem

1. What is a conditional multinomial problem?

A conditional multinomial problem is a type of statistical problem in which the outcome of interest has more than two categories and is influenced by one or more independent variables. This means that the probability of each category is dependent on the values of the independent variables.

2. How is a conditional multinomial problem different from a regular multinomial problem?

In a regular multinomial problem, the outcome of interest is not influenced by any independent variables, whereas in a conditional multinomial problem, the outcome is dependent on one or more independent variables. This adds an extra layer of complexity to the problem and requires specialized statistical techniques to analyze.

3. What are some common examples of conditional multinomial problems?

Conditional multinomial problems are commonly encountered in fields such as marketing, where the outcome of interest may be the purchase of a product and the independent variables could be demographic characteristics or advertising exposure. They are also commonly used in social sciences to study the relationship between multiple categorical variables, such as political party affiliation and voting behavior.

4. What are some techniques used to analyze conditional multinomial problems?

Some commonly used techniques for analyzing conditional multinomial problems include maximum likelihood estimation, multinomial logistic regression, and multinomial probit models. These techniques allow for the estimation of the probability of each outcome category based on the values of the independent variables.

5. What are some potential challenges when working with conditional multinomial problems?

One of the main challenges with conditional multinomial problems is the potential for multicollinearity, which occurs when the independent variables are highly correlated with each other. This can lead to inaccurate estimates and difficulties in interpreting the results. Additionally, interpreting the coefficients in these models can be more complex compared to simpler statistical models.

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