Parameter space for the negative binomial distribution

In summary, the negative binomial distribution is a member of the exponential family of distributions, with r known. The natural parameter space for this distribution is the values of \eta such that the sum of the exponential distribution converges. The series for this distribution can be found by substituting z = e^{\eta} into the general binomial series, and the constraint for \eta is |e^{\eta}| < 1. It is important to be cautious when relying on information from websites, as they may contain errors.
  • #1
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) [itex]P(X=x|r,p)= \binom {x-1}{r-1}p^{r}(1-p)^{x-r} [/itex] where [itex]x=r,r+1,... [/itex], or
2) [itex]P(Y=y|r,p)= \binom {y+r-1}{y}p^{r}(1-p)^{y} [/itex] where [itex]y=0,1,... [/itex].

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...

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

The parameter space are the values of [itex]\eta [/itex] such that [itex]\sum_A h(x) exp(\sum_{i=1}^{k} \eta_i t_i(x)) < \infty [/itex] where [itex]A [/itex] 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

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

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

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

Any help would be appreciated.
 
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  • #2
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) [itex]P(X=x|r,p)= \binom {x-1}{r-1}p^{r}(1-p)^{x-r} [/itex] where [itex]x=r,r+1,... [/itex], or
2) [itex]P(Y=y|r,p)= \binom {y+r-1}{y}p^{r}(1-p)^{y} [/itex] where [itex]y=0,1,... [/itex].

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...

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

The parameter space are the values of [itex]\eta [/itex] such that [itex]\sum_A h(x) exp(\sum_{i=1}^{k} \eta_i t_i(x)) < \infty [/itex] where [itex]A [/itex] 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

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

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

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

Any help would be appreciated.

[tex]\frac{1}{(1-z)^r} \equiv (1-z)^{-r} = \sum_{k=0}^{\infty} \binom{r+k-1}{k} z^k.[/tex]
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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  • #3
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:

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

Then letting [itex]w_1(p) = \eta [/itex] and solving the series for [itex]\eta [/itex]:

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

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

So I have [itex] (1-e^{\eta})^r [/itex].

I'm not sure, but I think the constraint is that [itex] |e^{\eta}| < 1 [/itex].

Is this right?
 
  • #4
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:

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

Then letting [itex]w_1(p) = \eta [/itex] and solving the series for [itex]\eta [/itex]:

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

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

So I have [itex] (1-e^{\eta})^r [/itex].

I'm not sure, but I think the constraint is that [itex] |e^{\eta}| < 1 [/itex].

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##.
 
  • #5
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,

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

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

[itex] \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} [/itex]

My question is this: Is the webpage wrong?
 
  • #6
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:

1. What is the negative binomial distribution?

The negative binomial distribution is a probability distribution that describes the number of successes in a sequence of independent and identically distributed Bernoulli trials before a specified number of failures occurs. It is often used to model count data, such as the number of accidents in a day or the number of customers arriving at a store.

2. What is the difference between the negative binomial and the binomial distribution?

The binomial distribution models the number of successes in a fixed number of trials, while the negative binomial distribution models the number of trials needed to achieve a specified number of successes. In other words, the binomial distribution has a fixed number of trials, while the negative binomial distribution has a fixed number of successes.

3. What is the parameter space for the negative binomial distribution?

The parameter space for the negative binomial distribution consists of two parameters: the probability of success for each trial (denoted by p) and the number of failures before the desired number of successes is achieved (denoted by r). The values of p must be between 0 and 1, and r must be a positive integer.

4. How is the negative binomial distribution related to other distributions?

The negative binomial distribution is closely related to the Poisson distribution and the gamma distribution. When the number of trials is large and the probability of success is small, the negative binomial distribution approximates the Poisson distribution. When the number of trials is large and the probability of success is close to 1, the negative binomial distribution approximates the gamma distribution.

5. What are some real-life applications of the negative binomial distribution?

The negative binomial distribution is commonly used in fields such as epidemiology, finance, and sports analytics. It can be used to model the number of disease cases in a population, the number of financial transactions before a desired profit is achieved, and the number of games a team needs to win to qualify for a tournament, among many other applications.

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