Parameter space for the negative binomial distribution

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Homework Help Overview

The discussion revolves around the natural parameter space for the negative binomial distribution, specifically when the parameter r is known. Participants explore the probability mass function (pmf) and its representation as a member of the exponential family of distributions.

Discussion Character

  • Conceptual clarification, Mathematical reasoning, Assumption checking

Approaches and Questions Raised

  • Participants attempt to rewrite the pmf in exponential form and analyze the convergence of related series. Questions arise regarding the constraints on parameters and the interpretation of the series involved.

Discussion Status

There is ongoing exploration of the parameter space and the conditions under which certain series converge. Some participants express uncertainty about the constraints on the parameters, while others reference external sources for clarification.

Contextual Notes

Participants note discrepancies in information sourced from external websites, questioning the validity of certain constraints related to the series they are analyzing.

Mogarrr
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Homework Statement


For the negative binomial distribution, with r known, describe the natural parameter space


Homework Equations


the pmf for the negative binomial distribution with parameters r and p can be
1) P(X=x|r,p)= \binom {x-1}{r-1}p^{r}(1-p)^{x-r} where x=r,r+1,..., or
2) P(Y=y|r,p)= \binom {y+r-1}{y}p^{r}(1-p)^{y} where y=0,1,....

A distribution, like the one above where r is known, is a member of the exponential family of distributions. An exponential distribution is one that can be expressed as...

h(x)c^{*}(\eta) exp(\sum_{i=1}^{k} \eta_i t_i(x))

The parameter space are the values of \eta such that \sum_A h(x) exp(\sum_{i=1}^{k} \eta_i t_i(x)) < \infty where A is the support of the pmf.

The Attempt at a Solution


Rewriting the 2nd pmf for the negative binomial distribution, as an exponential distribution, I have

h(y) = \binom {y+r-1}{y}, c(p) = p^{r} \cdot I_(0,1)(p), t_1(y)=y, and w_1(p) = ln(1-p).

Then I let \eta = w_1(p), and find the values for \eta where the sum converges.

I have \sum_{y=0}^{\infty} \binom{r+y-1}{y}(e^{\eta})^{y}, and I don't recognize this sum as anything that converges.

Any help would be appreciated.
 
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Mogarrr said:

Homework Statement


For the negative binomial distribution, with r known, describe the natural parameter space


Homework Equations


the pmf for the negative binomial distribution with parameters r and p can be
1) P(X=x|r,p)= \binom {x-1}{r-1}p^{r}(1-p)^{x-r} where x=r,r+1,..., or
2) P(Y=y|r,p)= \binom {y+r-1}{y}p^{r}(1-p)^{y} where y=0,1,....

A distribution, like the one above where r is known, is a member of the exponential family of distributions. An exponential distribution is one that can be expressed as...

h(x)c^{*}(\eta) exp(\sum_{i=1}^{k} \eta_i t_i(x))

The parameter space are the values of \eta such that \sum_A h(x) exp(\sum_{i=1}^{k} \eta_i t_i(x)) < \infty where A is the support of the pmf.

The Attempt at a Solution


Rewriting the 2nd pmf for the negative binomial distribution, as an exponential distribution, I have

h(y) = \binom {y+r-1}{y}, c(p) = p^{r} \cdot I_(0,1)(p), t_1(y)=y, and w_1(p) = ln(1-p).

Then I let \eta = w_1(p), and find the values for \eta where the sum converges.

I have \sum_{y=0}^{\infty} \binom{r+y-1}{y}(e^{\eta})^{y}, and I don't recognize this sum as anything that converges.

Any help would be appreciated.

\frac{1}{(1-z)^r} \equiv (1-z)^{-r} = \sum_{k=0}^{\infty} \binom{r+k-1}{k} z^k.
The reason for the name "negative binomial distribution" is that it comes from "negative binomial coefficients" ##\binom{-r}{k} = (-1)^k \binom{r+k-1}{k}## associated with the "negative binomial" series ##(1-z)^{-r}##. You ought to be able to show that the series converges for ##|z| < 1## because it just generalizes the series for ##1/(1-z)##.
 
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That look's like a winner.

I had thought of using the 1st pmf for the negative binomial distribution. Then I'd have the exponential distribution as:

h(x) = \binom {x-1}{r-1} \cdot I_{(r,r+1,...)}(x), c(p) = p^{r} \cdot I_{(0,1)}, t_1(x) = x-r, and w_1(p) = ln(1-p).

Then letting w_1(p) = \eta and solving the series for \eta:

\sum_X \binom {x-r}{r-1} e^{(x-r)\eta} = \sum_X \binom {x-r}{r-1} (e^{\eta})^{x-r},

then I recognized this series: \sum_{x=r}^{\infty} \binom {x-1}{r-1}w^{x-r} = (1-w)^{r}.

So I have (1-e^{\eta})^r.

I'm not sure, but I think the constraint is that |e^{\eta}| &lt; 1.

Is this right?
 
Mogarrr said:
That look's like a winner.

I had thought of using the 1st pmf for the negative binomial distribution. Then I'd have the exponential distribution as:

h(x) = \binom {x-1}{r-1} \cdot I_{(r,r+1,...)}(x), c(p) = p^{r} \cdot I_{(0,1)}, t_1(x) = x-r, and w_1(p) = ln(1-p).

Then letting w_1(p) = \eta and solving the series for \eta:

\sum_X \binom {x-r}{r-1} e^{(x-r)\eta} = \sum_X \binom {x-r}{r-1} (e^{\eta})^{x-r},

then I recognized this series: \sum_{x=r}^{\infty} \binom {x-1}{r-1}w^{x-r} = (1-w)^{r}.

So I have (1-e^{\eta})^r.

I'm not sure, but I think the constraint is that |e^{\eta}| &lt; 1.

Is this right?

I cannot make any sense out of what you write above.

Your previous post had a simple, explicit question, and I answered it. If you put ##z = e^{\eta}## you will get the summation you had in your first post, and I already pointed out what restrictions apply to ##z##.
 
Thanks for your first reply. I thanked you for it.

Maybe my reply was lengthy and ambiguous. I asked you about the constraints for w in this series,

\sum_{x=r}^{\infty} \binom {x-1}{r-1} w^{x-r} = (1-w)^{r}, which I found at this webpage.

I think it's the same series you gave, with a change in the index, letting x=y+r, I have

\sum_{y=0}^{\infty} \binom {r+y-1}{y} z^{y} = \sum_{x=r}^{\infty} \binom {x-1}{x-r}z^{x-r} = \sum_{x=r}^{\infty} \binom {x-1}{r-1}z^{x-r}

My question is this: Is the webpage wrong?
 
Looking at the negative binomial distribution as a particular case of the general binomial series, I see the formula on the webpage was wrong.

Note to self: do not trust all websites.
 
Last edited:

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