Solve for the covariance in the bivariate Poisson distribution

In summary: This means that for the bivariate Poisson distribution given above, the correlation between the two variables is bounded by the square root of the product of the two thetas, which are the parameters of the distribution. In summary, the correlation between the two variables in this distribution has a maximum and minimum bound based on the square root of the product of the parameters.
  • #1
GeorgeK
3
0
Dear All,

The bivariate Poisson distribution is as follows,
[tex]
\[ f(y_{s},y_{t})=e^{-(\theta_{s} + \theta_{t}+\theta_{st})}\frac{\theta_{s}^{y_{s}}}{y_{s}!}\frac{\theta_{t}^{y_{t}}}{y_{t}!}
\sum_{k=0}^{min(y_{s},y_{t})} \binom{y_{s}}{k} \binom{y_{t}}{k} k!\left(\frac{\theta_{st}}{\theta_{s} \theta_{t}}\right)^k.\]
[/tex]

Given that [tex] f(y_{s},y_{t}) >= 0 [/tex], solve for [tex] \theta_{st} [/tex].

Many thanks in advance,

George
 
Last edited:
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  • #2
Can someone please advise or give a comment or ask for more information if my question is not clear? I urgently/desperately need to know if this is solveable and how?

Many thanks in advance,

George
 
  • #3
I am not familiar with the equation, but I wonder if there is a "solution". If the variables are correlated, the correlation (within bounds) is anything.
 
  • #4
mathman said:
I am not familiar with the equation, but I wonder if there is a "solution". If the variables are correlated, the correlation (within bounds) is anything.

Right mathman (and of course to everyone else),

I am actually interested in finding what the bounds would be for the correlation but then [I thought] I first need to solve for the covariance. So, the reformed question is:

What are the bounds (maximum and minimum) for the correlation based on this bivariate Poisson Distribution?

George
 
  • #5
In general, the absolute value of a covariance is bounded by the square root of the product of the variances.
 

1. What is the bivariate Poisson distribution?

The bivariate Poisson distribution is a probability distribution that models the number of occurrences of two independent events in a given time or space. It is an extension of the univariate Poisson distribution, which only models one event.

2. How is covariance defined in the bivariate Poisson distribution?

Covariance in the bivariate Poisson distribution is a measure of the relationship between the two independent events. It is defined as the expected value of the product of the deviations from the respective means of the two events.

3. What is the formula for calculating covariance in the bivariate Poisson distribution?

The formula for covariance in the bivariate Poisson distribution is:
Cov(X,Y) = λ1λ2(1-e)
where λ1 and λ2 are the means of the two events and α is the correlation coefficient between them.

4. How does the covariance affect the shape of the bivariate Poisson distribution?

The covariance in the bivariate Poisson distribution affects the shape by determining the degree of scatter or clustering of the two events. A positive covariance indicates a positive relationship and results in a distribution with a higher peak and wider tails. A negative covariance indicates a negative relationship and results in a distribution with a lower peak and narrower tails.

5. Can the covariance in the bivariate Poisson distribution be negative?

Yes, the covariance in the bivariate Poisson distribution can be negative. This indicates a negative relationship between the two events, where one event tends to occur less frequently when the other event occurs more frequently.

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