Index: The Book of Statistical ProofsStatistical Models ▷ Poisson data ▷ Poisson-distributed data ▷ Maximum likelihood estimation

Theorem: Let there be a Poisson-distributed data set $y = \left\lbrace y_1, \ldots, y_n \right\rbrace$:

\[\label{eq:Poiss} y_i \sim \mathrm{Poiss}(\lambda), \quad i = 1, \ldots, n \; .\]

Then, the maximum likelihood estimate for the rate parameter $\lambda$ is given by

\[\label{eq:Poiss-MLE} \hat{\lambda} = \bar{y}\]

where $\bar{y}$ is the sample mean

\[\label{eq:y-mean} \bar{y} = \frac{1}{n} \sum_{i=1}^n y_i \; .\]

Proof: The likelihood function for each observation is given by the probability mass function of the Poisson distribution

\[\label{eq:Poiss-yi} p(y_i|\lambda) = \mathrm{Poiss}(y_i; \lambda) = \frac{\lambda^{y_i} \cdot \exp(-\lambda)}{y_i !}\]

and because observations are independent, the likelihood function for all observations is the product of the individual ones:

\[\label{eq:Poiss-LF} p(y|\lambda) = \prod_{i=1}^n p(y_i|\lambda) = \prod_{i=1}^n \frac{\lambda^{y_i} \cdot \exp(-\lambda)}{y_i !} \; .\]

Thus, the log-likelihood function is

\[\label{eq:Poiss-LL} \mathrm{LL}(\lambda) = \log p(y|\lambda) = \log \left[ \prod_{i=1}^n \frac{\lambda^{y_i} \cdot \exp(-\lambda)}{y_i !} \right]\]

which can be developed into

\[\label{eq:Poiss-LL-der} \begin{split} \mathrm{LL}(\lambda) &= \sum_{i=1}^n \log \left[ \frac{\lambda^{y_i} \cdot \exp(-\lambda)}{y_i !} \right] \\ &= \sum_{i=1}^n \left[ y_i \cdot \log(\lambda) - \lambda - \log(y_i !) \right] \\ &= - \sum_{i=1}^n \lambda + \sum_{i=1}^n y_i \cdot \log(\lambda) - \sum_{i=1}^n \log(y_i !) \\ &= - n \lambda + \log(\lambda) \sum_{i=1}^n y_i - \sum_{i=1}^n \log(y_i !) \\ \end{split}\]

The derivatives of the log-likelihood with respect to $\lambda$ are

\[\label{eq:Poiss-dLLdl-d2LLdl2} \begin{split} \frac{\mathrm{d}\mathrm{LL}(\lambda)}{\mathrm{d}\lambda} &= \frac{1}{\lambda} \sum_{i=1}^n y_i - n \\ \frac{\mathrm{d}^2\mathrm{LL}(\lambda)}{\mathrm{d}\lambda^2} &= -\frac{1}{\lambda^2} \sum_{i=1}^n y_i \; . \\ \end{split}\]

Setting the first derivative to zero, we obtain:

\[\label{eq:Poiss-dLLdl} \begin{split} \frac{\mathrm{d}\mathrm{LL}(\hat{\lambda})}{\mathrm{d}\lambda} &= 0 \\ 0 &= \frac{1}{\hat{\lambda}} \sum_{i=1}^n y_i - n \\ \hat{\lambda} &= \frac{1}{n} \sum_{i=1}^n y_i = \bar{y} \; . \end{split}\]

Plugging this value into the second derivative, we confirm:

\[\label{eq:Poiss-d2LLdl2} \begin{split} \frac{\mathrm{d}^2\mathrm{LL}(\hat{\lambda})}{\mathrm{d}\lambda^2} &= -\frac{1}{\bar{y}^2} \sum_{i=1}^n y_i \\ &= -\frac{n \cdot \bar{y}}{\bar{y}^2} \\ &= -\frac{n}{\bar{y}} < 0 \; . \end{split}\]

This demonstrates that the estimate $\hat{\lambda} = \bar{y}$ maximizes the likelihood $p(y \vert \lambda)$.

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Metadata: ID: P27 | shortcut: poiss-mle | author: JoramSoch | date: 2020-01-20, 21:53.