Germán Rodríguez
Generalized Linear Models Princeton University

B. Generalized Linear Model Theory

We describe the generalized linear model as formulated by Nelder and Wedderburn (1972), and discuss estimation of the parameters and tests of hypotheses.

B.1 The Model

Let \( y_1, \ldots, y_n \) denote \( n \) independent observations on a response. We treat \( y_i \) as a realization of a random variable \( Y_i \). In the general linear model we assume that \( Y_i \) has a normal distribution with mean \( \mu_i \) and variance \( \sigma^2 \)

\[ Y_i \sim \mbox{N}(\mu_i, \sigma^2), \] and we further assume that the expected value \( \mu_i \) is a linear function of \( p \) predictors that take values \( \boldsymbol{x}_i' = (x_{i1}, \ldots, x_{ip}) \) for the \( i \)-th case, so that \[ \mu_i = \boldsymbol{x}_i' \boldsymbol{\beta}, \]

where \( \boldsymbol{\beta} \) is a vector of unknown parameters.

We will generalize this in two steps, dealing with the stochastic and systematic components of the model.

B.1.1 The Exponential Family

We will assume that the observations come from a distribution in the exponential family with probability density function

\[\tag{B.1}f(y_i) = \exp\{\frac{y_i \theta_i - b(\theta_i)}{a_i(\phi)} + c(y_i, \phi) \}.\]

Here \( \theta_i \) and \( \phi \) are parameters and \( a_i(\phi) \), \( b(\theta_i) \) and \( c(y_i, \phi) \) are known functions. In all models considered in these notes the function \( a_i(\phi) \) has the form

\[ a_i(\phi) = \phi/p_i, \]

where \( p_i \) is a known prior weight, usually 1.

The parameters \( \theta_i \) and \( \phi \) are essentially location and scale parameters. It can be shown that if \( Y_i \) has a distribution in the exponential family then it has mean and variance

\[\tag{B.2}\begin{eqnarray}\mbox{E}(Y_i)& =& \mu_i = b'(\theta_i)\\mbox{var}(Y_i)& =& \sigma^2_i = b''(\theta_i) a_i(\phi),\end{eqnarray}\]

where \( b'(\theta_i) \) and \( b''(\theta_i) \) are the first and second derivatives of \( b(\theta_i) \). When \( a_i(\phi)=\phi/p_i \) the variance has the simpler form

\[ \mbox{var}(Y_i) = \sigma^2_i = \phi b''(\theta_i)/p_i. \]

The exponential family just defined includes as special cases the normal, binomial, Poisson, exponential, gamma and inverse Gaussian distributions.

Example: The normal distribution has density \[ f(y_i) = \frac{1}{\sqrt{2\pi\sigma^2}} \exp\{-\frac{1}{2}\frac{(y_i-\mu_i)^2}{\sigma^2}\}. \]

Expanding the square in the exponent we get \( (y_i-\mu_i)^2 = y_i^2 + \mu_i^2 - 2 y_i \mu_i \), so the coefficient of \( y_i \) is \( \mu_i/\sigma^2 \). This result identifies \( \theta_i \) as \( \mu_i \) and \( \phi \) as \( \sigma^2 \), with \( a_i(\phi)=\phi \). Now write

\[ f(y_i) = \exp\{ \frac{y_i \mu_i-\frac{1}{2}\mu_i^2}{\sigma^2} - \frac{y_i^2}{2\sigma^2} - \frac{1}{2}\log(2\pi\sigma^2)\}. \]

This shows that \( b(\theta_i)=\frac{1}{2}\theta_i^2 \) (recall that \( \theta_i=\mu_i \)). Let us check the mean and variance:

\[\tag{B.3}\begin{eqnarray*}E(Y_i) = b'(\theta_i) = \theta_i = \mu_i,\\mbox{var}(Y_i) = b''(\theta_i)a_i(\phi) = \sigma^2.\end{eqnarray*}\]

Try to generalize this result to the case where \( Y_i \) has a normal distribution with mean \( \mu_i \) and variance \( \sigma^2/n_i \) for known constants \( n_i \), as would be the case if the \( Y_i \) represented sample means.\( \Box \)

Example: In Problem Set 1 you will show that the exponential distribution with density \[ f(y_i)= \lambda_i \exp\{ -\lambda_i y_i\} \]

belongs to the exponential family.\( \Box \)

In Sections B.4 and B.5 we verify that the binomial and Poisson distributions also belong to this family.

B.1.2 The Link Function

The second element of the generalization is that instead of modeling the mean, as before, we will introduce a one-to-one continuous differentiable transformation \( g(\mu_i) \) and focus on

\[\tag{B.4}\eta_i = g(\mu_i).\]

The function \( g(\mu_i) \) will be called the link\/ function. Examples of link functions include the identity, log, reciprocal, logit and probit.

We further assume that the transformed mean follows a linear model, so that

\[\tag{B.5}\eta_i = \boldsymbol{x}_i' \boldsymbol{\beta}.\]

The quantity \( \eta_i \) is called the linear predictor. Note that the model for \( \eta_i \) is pleasantly simple. Since the link function is one-to-one we can invert it to obtain

\[ \mu_i = g^{-1}(\boldsymbol{x}_i' \boldsymbol{\beta}). \]

The model for \( \mu_i \) is usually more complicated than the model for \( \eta_i \).

Note that we do not transform the response \( y_i \), but rather its expected value \( \mu_i \). A model where \( \log y_i \) is linear on \( x_i \), for example, is not the same as a generalized linear model where \( \log \mu_i \) is linear on \( x_i \).

Example: The standard linear model we have studied so far can be described as a generalized linear model with normal errors and identity link, so that \[ \eta_i = \mu_i. \]

It also happens that \( \mu_i \), and therefore \( \eta_i \), is the same as \( \theta_i \), the parameter in the exponential family density.\( \Box \)

When the link function makes the linear predictor \( \eta_i \) the same as the canonical parameter \( \theta_i \), we say that we have a canonical link\/. The identity is the canonical link for the normal distribution. In later sections we will see that the logit is the canonical link for the binomial distribution and the log is the canonical link for the Poisson distribution. This leads to some natural pairings:


However, other combinations are also possible. An advantage of canonical links is that a minimal sufficient statistic for \( \boldsymbol{\beta} \) exists, i.e. all the information about \( \boldsymbol{\beta} \) is contained in a function of the data of the same dimensionality as \( \boldsymbol{\beta} \).

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