# Solved – What log-likelihood function do you use when doing a Poisson regression with continuous response

I am given \$N\$ observations of pairs of covariates and response \$(mathbf{x}_i, y_i)\$. When the response are non-negative integers, by doing Poisson regression I am modelling \$y_i sim mathrm{Pois}(mu_i)\$ as Poisson random variables with mean \$mu_i\$, such that \$ln(mu_i)\$ is a linear function of the covariate \$mathbf{x}_i\$. A maximum likelihood estimator for the coefficients of \$mathbf{x}_i\$ maximises the Poisson log-likelihood:

\$\$sum_{i=1}^N (y_i ln(mu_i) – mu_i)\$\$

I have seen references to doing Poisson regression with non-negative, non-integers, e.g. How does a Poisson distribution work when modeling continuous data and does it result in information loss?

In this case, what log-likelihood function is used? Do you still use the above function but allow \$y_i\$ to take the non-integer values?

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You are right. In fact, since proper Poisson model would be incorrect in here because of dealing with continuous outcome, you'll be using quasi-Poisson model.

It is called quasi-likelihood and was described for the first time by Wedderburn (1974):

To define a likelihood we have to specify the form of distribution of the observations, but to define a quasi-likelihood function we need only specify a relation between the mean and variance of the observations and the quasi-likelihood can then be used for estimation.

You can find some more description and examples in paper by McCullagh (1983) and handbooks on GLM's.

In case of quasi-Poisson model, the quasi-likelihood is

\$\$ y log mu – mu \$\$

where \$y ge 0\$ and \$mu > 0\$ as described in McCullagh (1983).

Wedderburn, R. W. (1974). Quasi-likelihood functions, generalized linear models, and the Gauss—Newton method. Biometrika, 61(3), 439-447.

McCullagh, P. (1983). Quasi-likelihood functions. The Annals of Statistics, 59-67.

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# Solved – What log-likelihood function do you use when doing a Poisson regression with continuous response

I am given \$N\$ observations of pairs of covariates and response \$(mathbf{x}_i, y_i)\$. When the response are non-negative integers, by doing Poisson regression I am modelling \$y_i sim mathrm{Pois}(mu_i)\$ as Poisson random variables with mean \$mu_i\$, such that \$ln(mu_i)\$ is a linear function of the covariate \$mathbf{x}_i\$. A maximum likelihood estimator for the coefficients of \$mathbf{x}_i\$ maximises the Poisson log-likelihood:

\$\$sum_{i=1}^N (y_i ln(mu_i) – mu_i)\$\$

I have seen references to doing Poisson regression with non-negative, non-integers, e.g. How does a Poisson distribution work when modeling continuous data and does it result in information loss?

In this case, what log-likelihood function is used? Do you still use the above function but allow \$y_i\$ to take the non-integer values?

You are right. In fact, since proper Poisson model would be incorrect in here because of dealing with continuous outcome, you'll be using quasi-Poisson model.

It is called quasi-likelihood and was described for the first time by Wedderburn (1974):

To define a likelihood we have to specify the form of distribution of the observations, but to define a quasi-likelihood function we need only specify a relation between the mean and variance of the observations and the quasi-likelihood can then be used for estimation.

You can find some more description and examples in paper by McCullagh (1983) and handbooks on GLM's.

In case of quasi-Poisson model, the quasi-likelihood is

\$\$ y log mu – mu \$\$

where \$y ge 0\$ and \$mu > 0\$ as described in McCullagh (1983).

Wedderburn, R. W. (1974). Quasi-likelihood functions, generalized linear models, and the Gauss—Newton method. Biometrika, 61(3), 439-447.

McCullagh, P. (1983). Quasi-likelihood functions. The Annals of Statistics, 59-67.

Rate this post