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Assume I have a random variable $X \sim Poisson(\lambda)$ which models the potential nr of people entering some room. Now consider this room has a capacity $c$ so that whenever $X > c$ we observe $c$, so basically we consider the random variable $Y = min(c, X)$.

Now if we consider that $\lambda$ is fairly small, say $\lambda = 5$ and we consider that $c = 5$ then if I point estimate by taking the mean of $Y$ I will get some value $y \approx4$.
However, if I take the original room and put in a bunch of walls to make sub-rooms where each sub-room now has a capacity of $c=1$ and I divide up the intensity between these new rooms so that $\lambda = 1$ for each room and then point estimate by the mean again I get values $y_i \approx0.6, i = 1,...,5$. If I sum these different means I will get $y \approx3$

So by putting in those walls in my room and uniformly divided up the intensity between them I have essentially "lost" 1 whole expected person entering. I've been trying to wrap my head around it but haven't been able to come up with a satisfying answer of

  1. How this is bad modelling (which I assume it is)
  2. How to model this kind of situation better

Any ideas?

Also, a snippet of code in R to illustrate:

f <- function(lambda, C) {
  lambda * ppois(C-2,lambda) + C * ppois(C-1, lambda, lower.tail=FALSE)
}

censored1 = f(1, 1)
censored2 = f(5, 5)

list("manySmallRooms" = censored1*5, "oneBiggerRoom" = censored2)

$manySmallRooms
[1] 3.160603

$oneBiggerRoom
[1] 4.122663
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  • $\begingroup$ You have a right-censored Poisson, so the sample mean is not a good estimator of the parameter $\lambda$. This might help: jstor.org/stable/2285533 $\endgroup$ Commented Mar 22 at 10:30
  • $\begingroup$ I'm not interested in estimating $\lambda$ as such but rather in some kind of point estimate of the nr of people I expect to see in these rooms and that this estimate would agree whether I arbitrarily divide up one room into many smaller or not as long as the total intensity is the same. $\endgroup$ Commented Mar 22 at 12:05
  • $\begingroup$ Thanks, I understand now. I've posted an answer. $\endgroup$ Commented Mar 22 at 14:38

1 Answer 1

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The two are not equivalent. Think of it this way:

Case A: There is one big queue to get into the single room, where the number of arrivals is $\mathrm{Poisson}(5)$.

Case B: There are five separate queues (one for each sub-room), each of which is $\mathrm{Poisson}(1)$.

While it is true that the distribution of the total number of arrivals is the same in both cases, suppose that there are exactly two arrivals. How many of them can get in a room?

In Case A the capacity of the room is 5, so both people can get in.

In Case B if the two people happen to queue for the same room, only one of them can get in.

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  • $\begingroup$ I have noticed that this is more of an issue when $\lambda$ is small due to the non-symmetry of the poisson, while when $\lambda$ is large the two cases pretty much aligns. So do you have suggestions on how to better model this? $\endgroup$ Commented Mar 24 at 9:51
  • $\begingroup$ Check out the answer by @whuber here: stats.stackexchange.com/questions/363181/… $\endgroup$ Commented Mar 24 at 10:38
  • $\begingroup$ It explains how to compute the quantity you are interested in. $\endgroup$ Commented Mar 24 at 10:39

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