Binomial regression

Binomial regression is closely related to binary regression: a binary regression can be considered a binomial regression with ${\displaystyle n=1}$, or a regression on

In one published example of an application of binomial regression,[3] the details were as follows. The observed outcome variable was whether or not a fault occurred in an industrial process. There were two explanatory variables: the first was a simple two-case factor representing whether or not a modified version of the process was used and the second was an ordinary quantitative variable measuring the purity of the material being supplied for the process.

The response variable Y is assumed to be binomially distributed conditional on the explanatory variables X. The number of trials n is known, and the probability of success for each trial p is specified as a function θ(X). This implies that the conditional expectation and conditional variance of the observed fraction of successes, Y/n, are

${\displaystyle E(Y/n\mid X)=\theta (X)}$

${\displaystyle \operatorname {Var} (Y/n\mid X)=\theta (X)(1-\theta (X))/n}$

The goal of binomial regression is to estimate the function θ(X). Typically the statistician assumes ${\displaystyle \theta (X)=m(\beta ^{\mathrm {T} }X)}$, for a known function m, and estimates β. Common choices for m include the logistic function.^[1]

The data are often fitted as a

${\displaystyle L({\boldsymbol {\mu }}\mid Y)=\prod _{i=1}^{n}\left(1_{y_{i}=1}(\mu _{i})+1_{y_{i}=0}(1-\mu _{i})\right),\,\!}$

where 1_A is the

Models used in binomial regression can often be extended to multinomial data.

There are many methods of generating the values of μ in systematic ways that allow for interpretation of the model; they are discussed below.

There is a requirement that the modelling linking the probabilities μ to the explanatory variables should be of a form which only produces values in the range 0 to 1. Many models can be fitted into the form

${\displaystyle {\boldsymbol {\mu }}=g({\boldsymbol {\eta }})\,.}$

Here η is an intermediate variable representing a linear combination, containing the regression parameters, of the explanatory variables. The function g is the cumulative distribution function (cdf) of some probability distribution. Usually this probability distribution has a support from minus infinity to plus infinity so that any finite value of η is transformed by the function g to a value inside the range 0 to 1.

In the case of logistic regression, the link function is the log of the odds ratio or logistic function. In the case of probit, the link is the cdf of the normal distribution. The linear probability model is not a proper binomial regression specification because predictions need not be in the range of zero to one; it is sometimes used for this type of data when the probability space is where interpretation occurs or when the analyst lacks sufficient sophistication to fit or calculate approximate linearizations of probabilities for interpretation.

Binomial regression is closely connected with binary regression. If the response is a

: "success" is 1 success out of 1 trial, while "failure" is 0 successes out of 1 trial. This can now be considered a binomial distribution with ${\displaystyle n=1}$ trial, so a binary regression is a special case of a binomial regression. If these data are

A binary choice model assumes a

${\displaystyle U_{n}={\boldsymbol {\beta }}\cdot \mathbf {s_{n}} +\varepsilon _{n}}$

where ${\displaystyle {\boldsymbol {\beta }}}$ is a set of

${\displaystyle \mathbf {s_{n}} }$

" or regular continuous variables. ${\displaystyle \varepsilon _{n}}$ is a

The person takes the action, y_n = 1, if U_n > 0. The unobserved term, ε_n, is assumed to have a logistic distribution.

The specification is written succinctly as:

• • U_n = βs_n + ε_n
  • ${\displaystyle Y_{n}={\begin{cases}1,&{\text{if }}U_{n}>0,\\0,&{\text{if }}U_{n}\leq 0\end{cases}}}$
  • ε ∼ logistic, standard normal, etc.

Let us write it slightly differently:

• • U_n = βs_n − e_n
  • ${\displaystyle Y_{n}={\begin{cases}1,&{\text{if }}U_{n}>0,\\0,&{\text{if }}U_{n}\leq 0\end{cases}}}$
  • e ∼ logistic, standard normal, etc.

Here we have made the substitution e_n = −ε_n. This changes a random variable into a slightly different one, defined over a negated domain. As it happens, the error distributions we usually consider (e.g. logistic distribution, standard normal distribution, standard Student's t-distribution, etc.) are symmetric about 0, and hence the distribution over e_n is identical to the distribution over ε_n.

Denote the cumulative distribution function (CDF) of ${\displaystyle e}$ as ${\displaystyle F_{e},}$ and the quantile function (inverse CDF) of ${\displaystyle e}$ as ${\displaystyle F_{e}^{-1}.}$

Note that

${\displaystyle {\begin{aligned}\Pr(Y_{n}=1)&=\Pr(U_{n}>0)\\[6pt]&=\Pr({\boldsymbol {\beta }}\cdot \mathbf {s_{n}} -e_{n}>0)\\[6pt]&=\Pr(-e_{n}>-{\boldsymbol {\beta }}\cdot \mathbf {s_{n}} )\\[6pt]&=\Pr(e_{n}\leq {\boldsymbol {\beta }}\cdot \mathbf {s_{n}} )\\[6pt]&=F_{e}({\boldsymbol {\beta }}\cdot \mathbf {s_{n}} )\end{aligned}}}$

Since ${\displaystyle Y_{n}}$ is a Bernoulli trial, where ${\displaystyle \mathbb {E} [Y_{n}]=\Pr(Y_{n}=1),}$ we have

${\displaystyle \mathbb {E} [Y_{n}]=F_{e}({\boldsymbol {\beta }}\cdot \mathbf {s_{n}} )}$

or equivalently

${\displaystyle F_{e}^{-1}(\mathbb {E} [Y_{n}])={\boldsymbol {\beta }}\cdot \mathbf {s_{n}} .}$

Note that this is exactly equivalent to the binomial regression model expressed in the formalism of the generalized linear model.

If ${\displaystyle e_{n}\sim {\mathcal {N}}(0,1),}$ i.e. distributed as a

${\displaystyle \Phi ^{-1}(\mathbb {E} [Y_{n}])={\boldsymbol {\beta }}\cdot \mathbf {s_{n}} }$

which is exactly a probit model.

If ${\displaystyle e_{n}\sim \operatorname {Logistic} (0,1),}$ i.e. distributed as a standard

${\displaystyle \operatorname {logit} (\mathbb {E} [Y_{n}])={\boldsymbol {\beta }}\cdot \mathbf {s_{n}} }$

which is exactly a

Note that the two different formalisms — generalized linear models (GLM's) and discrete choice models — are equivalent in the case of simple binary choice models, but can be extended if differing ways:

• GLM's can easily handle arbitrarily distributed
  error variable, which must by assumption have a probability distribution
  .
• On the other hand, because discrete choice models are described as types of generative models, it is conceptually easier to extend them to complicated situations with multiple, possibly correlated, choices for each person, or other variations.

A latent variable model involving a binomial observed variable Y can be constructed such that Y is related to the latent variable Y* via

${\displaystyle Y={\begin{cases}0,&{\mbox{if }}Y^{*}>0\\1,&{\mbox{if }}Y^{*}<0.\end{cases}}}$

The latent variable Y* is then related to a set of regression variables X by the model

${\displaystyle Y^{*}=X\beta +\epsilon \ .}$

This results in a binomial regression model.

The variance of ϵ can not be identified and when it is not of interest is often assumed to be equal to one. If ϵ is normally distributed, then a probit is the appropriate model and if ϵ is log-Weibull distributed, then a logit is appropriate. If ϵ is uniformly distributed, then a linear probability model is appropriate.

• Linear probability model
• Poisson regression
• Predictive modelling

• ISBN 0-412-16570-8
  .
• Rodríguez, Germán (2007). "Lecture Notes on Generalized Linear Models".

• Dean, C. B. (1992). "Testing for Overdispersion in Poisson and Binomial Regression Models". Journal of the American Statistical Association. 87 (418). Informa UK Limited: 451–457.
  JSTOR 2290276
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