# Re: [Rd] pbinom with size argument 0 (PR#8560)

From: Uffe Høgsbro Thygesen <uht_at_dfu.min.dk>
Date: Sun 05 Feb 2006 - 20:39:36 GMT

Hello all

A pragmatic argument for allowing size==0 is the situation where the size is in itself a random variable (that's how I stumbled over the inconsistency, by the way).

For example, in textbooks on probability it is stated that:

If X is Poisson(lambda), and the conditional   distribution of Y given X is Binomial(X,p), then   Y is Poisson(lambda*p).

(cf eg Pitman's "Probability", p. 400)

Clearly this statement requires Binomial(0,p) to be a well-defined distribution.

Such statements would be quite convoluted if we did not define Binomial(0,p) as a legal (but degenerate) distribution. The same applies to codes where the size parameter may attain the value 0.

Just my 2 cents.

Cheers,

Uffe

-----Oprindelig meddelelse-----
Fra: pd@pubhealth.ku.dk på vegne af Peter Dalgaard Sendt: sø 05-02-2006 01:33
Til: P Ehlers
Cc: ted.harding@nessie.mcc.ac.uk; Peter Dalgaard; R-bugs@biostat.ku.dk; r-devel@stat.math.ethz.ch; Uffe Høgsbro Thygesen Emne: Re: [Rd] pbinom with size argument 0 (PR#8560)

P Ehlers <ehlers@math.ucalgary.ca> writes:

> I prefer a (consistent) NaN. What happens to our notion of a
> Binomial RV as a sequence of Bernoulli RVs if we permit n=0?
> I have never seen (nor contemplated, I confess) the definition
> of a Bernoulli RV as anything other than some dichotomous-outcome
> one-trial random experiment.

What's the problem ??

An n=0 binomial is the sum of an empty set of Bernoulli RV's, and the sum over an empty set is identically 0.

> Not n trials, where n might equal zero,
> but _one_ trial. I can't see what would be gained by permitting a
> zero-trial experiment. If we assign probability 1 to each outcome,
> we have a problem with the sum of the probabilities.

Consistency is what you gain. E.g.

binom(.,n=n1+n2,p) == binom(.,n=n1,p) * binom(.,n=n2,p)

where * denotes convolution. This will also hold for n1=0 or n2=0 if the binomial in that case is defined as a one-point distribution at zero. Same thing as any(logical(0)) etc., really.

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Received on Mon Feb 06 07:46:17 2006

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