A popular parameterization of the negative binomial distribution is by $\mu$ and $r$, which represent mean and dispersion, respectively. The probability mass function states: $$ \text{P(x = k)} = \frac{\Gamma(r + k)}{k! \Gamma(r)} \cdot \Bigg( \frac{r}{r + \mu} \Bigg)^r \Bigg( \frac{\mu}{r + \mu} \Bigg)^k $$

A common way to infer population parameter $r, \mu$ from samples is to use maximum likelihood estimation.


There is a relationship between $\mu, \sigma^2, r$, which is $$ r = \frac{\mu^2}{\sigma^2 - \mu} $$

I was wondering if this would be a reasonable way to infer population parameters:

  1. calculate sample mean $\bar x$ and sample variance $s^2$
  2. infer the population dispersion as $$\hat r = \frac{\bar x^2}{s^2 - \bar x}$$

Yes, this is a reasonable way. It is called a "Method of Moments" (MoM) estimator, as it uses the first two sample moments to calculate the estimate. Often, for the negative binomial distribution, it's actually a pretty good estimator - but by no means always.

Having said this, though, it can easily be, especially with small samples and not much overdispersion, that $s^2 \leq \bar{x}$, in which case you have a problem. In this case, the MLE doesn't exist either. Other estimators can be found in Estimating the Negative Binomial Dispersion Parameter.

  • $\begingroup$ For the method of moments estimator, I will need to calculate sample variance first. Should I use "n-1" or "n" as the denominator? I understand the sample variance should be corrected for normal distribution, but should I also correct for negative binomial distribution? $\endgroup$
    – Taotao Tan
    Jan 16 at 4:48

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