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In statistics, a **generalized linear model** (**GLM**) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a *link function* and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.

- Intuition
- Overview
- Model components
- Probability distribution
- Linear predictor
- Link function
- Fitting
- Maximum likelihood
- Bayesian methods
- Examples
- General linear models
- Linear regression
- Binary data
- Multinomial regression
- Count data
- Extensions
- Correlated or clustered data
- Generalized additive models
- See also
- References
- Citations
- Bibliography
- Further reading
- External links

Generalized linear models were formulated by John Nelder and Robert Wedderburn as a way of unifying various other statistical models, including linear regression, logistic regression and Poisson regression.^{ [1] } They proposed an iteratively reweighted least squares method for maximum likelihood estimation of the model parameters. Maximum-likelihood estimation remains popular and is the default method on many statistical computing packages. Other approaches, including Bayesian approaches and least squares fits to variance stabilized responses, have been developed.

Ordinary linear regression predicts the expected value of a given unknown quantity (the *response variable*, a random variable) as a linear combination of a set of observed values (*predictors*). This implies that a constant change in a predictor leads to a constant change in the response variable (i.e. a *linear-response model*). This is appropriate when the response variable can vary, to a good approximation, indefinitely in either direction, or more generally for any quantity that only varies by a relatively small amount compared to the variation in the predictive variables, e.g. human heights.

However, these assumptions are inappropriate for some types of response variables. For example, in cases where the response variable is expected to be always positive and varying over a wide range, constant input changes lead to geometrically (i.e. exponentially) varying, rather than constantly varying, output changes. As an example, suppose a linear prediction model learns from some data (perhaps primarily drawn from large beaches) that a 10 degree temperature decrease would lead to 1,000 fewer people visiting the beach. This model is unlikely to generalize well over different sized beaches. More specifically, the problem is that if you use the model to predict the new attendance with a temperature drop of 10 for a beach that regularly receives 50 beachgoers, you would predict an impossible attendance value of −950. Logically, a more realistic model would instead predict a constant *rate* of increased beach attendance (e.g. an increase in 10 degrees leads to a doubling in beach attendance, and a drop in 10 degrees leads to a halving in attendance). Such a model is termed an *exponential-response model* (or * log-linear model *, since the logarithm of the response is predicted to vary linearly).

Similarly, a model that predicts a probability of making a yes/no choice (a Bernoulli variable) is even less suitable as a linear-response model, since probabilities are bounded on both ends (they must be between 0 and 1). Imagine, for example, a model that predicts the likelihood of a given person going to the beach as a function of temperature. A reasonable model might predict, for example, that a change in 10 degrees makes a person two times more or less likely to go to the beach. But what does "twice as likely" mean in terms of a probability? It cannot literally mean to double the probability value (e.g. 50% becomes 100%, 75% becomes 150%, etc.). Rather, it is the * odds * that are doubling: from 2:1 odds, to 4:1 odds, to 8:1 odds, etc. Such a model is a *log-odds or logistic model*.

Generalized linear models cover all these situations by allowing for response variables that have arbitrary distributions (rather than simply normal distributions), and for an arbitrary function of the response variable (the *link function*) to vary linearly with the predictors (rather than assuming that the response itself must vary linearly). For example, the case above of predicted number of beach attendees would typically be modeled with a Poisson distribution and a log link, while the case of predicted probability of beach attendance would typically be modeled with a Bernoulli distribution (or binomial distribution, depending on exactly how the problem is phrased) and a log-odds (or * logit *) link function.

In a generalized linear model (GLM), each outcome **Y** of the dependent variables is assumed to be generated from a particular distribution in an exponential family, a large class of probability distributions that includes the normal, binomial, Poisson and gamma distributions, among others. The mean, * μ*, of the distribution depends on the independent variables,

where E(**Y**|**X**) is the expected value of **Y** conditional on **X**; **X β** is the

In this framework, the variance is typically a function, **V**, of the mean:

It is convenient if **V** follows from an exponential family of distributions, but it may simply be that the variance is a function of the predicted value.

The unknown parameters, * β*, are typically estimated with maximum likelihood, maximum quasi-likelihood, or Bayesian techniques.

The GLM consists of three elements:

- 1. A particular distribution for modeling from among those which are considered exponential families of probability distributions,
- 2. A linear predictor , and
- 3. A link function such that .

An **overdispersed exponential family** of distributions is a generalization of an exponential family and the exponential dispersion model of distributions and includes those families of probability distributions, parameterized by and , whose density functions *f* (or probability mass function, for the case of a discrete distribution) can be expressed in the form

The *dispersion parameter*, , typically is known and is usually related to the variance of the distribution. The functions , , , , and are known. Many common distributions are in this family, including the normal, exponential, gamma, Poisson, Bernoulli, and (for fixed number of trials) binomial, multinomial, and negative binomial.

For scalar and (denoted and in this case), this reduces to

is related to the mean of the distribution. If is the identity function, then the distribution is said to be in canonical form (or *natural form*). Note that any distribution can be converted to canonical form by rewriting as and then applying the transformation . It is always possible to convert in terms of the new parametrization, even if is not a one-to-one function; see comments in the page on exponential families. If, in addition, is the identity and is known, then is called the *canonical parameter* (or *natural parameter*) and is related to the mean through

For scalar and , this reduces to

Under this scenario, the variance of the distribution can be shown to be^{ [2] }

For scalar and , this reduces to

The linear predictor is the quantity which incorporates the information about the independent variables into the model. The symbol *η* (Greek "eta") denotes a linear predictor. It is related to the expected value of the data through the link function.

*η* is expressed as linear combinations (thus, "linear") of unknown parameters * β*. The coefficients of the linear combination are represented as the matrix of independent variables

The link function provides the relationship between the linear predictor and the mean of the distribution function. There are many commonly used link functions, and their choice is informed by several considerations. There is always a well-defined *canonical* link function which is derived from the exponential of the response's density function. However, in some cases it makes sense to try to match the domain of the link function to the range of the distribution function's mean, or use a non-canonical link function for algorithmic purposes, for example Bayesian probit regression.

When using a distribution function with a canonical parameter , the canonical link function is the function that expresses in terms of , i.e. . For the most common distributions, the mean is one of the parameters in the standard form of the distribution's density function, and then is the function as defined above that maps the density function into its canonical form. When using the canonical link function, , which allows to be a sufficient statistic for .

Following is a table of several exponential-family distributions in common use and the data they are typically used for, along with the canonical link functions and their inverses (sometimes referred to as the mean function, as done here).

Distribution | Support of distribution | Typical uses | Link name | Link function, | Mean function |
---|---|---|---|---|---|

Normal | real: | Linear-response data | Identity | ||

Exponential | real: | Exponential-response data, scale parameters | Negative inverse | ||

Gamma | |||||

Inverse Gaussian | real: | Inverse squared | |||

Poisson | integer: | count of occurrences in fixed amount of time/space | Log | ||

Bernoulli | integer: | outcome of single yes/no occurrence | Logit | ||

Binomial | integer: | count of # of "yes" occurrences out of N yes/no occurrences | |||

Categorical | integer: | outcome of single K-way occurrence | |||

K-vector of integer: , where exactly one element in the vector has the value 1 | |||||

Multinomial | K-vector of integer: | count of occurrences of different types (1 .. K) out of N total K-way occurrences |

In the cases of the exponential and gamma distributions, the domain of the canonical link function is not the same as the permitted range of the mean. In particular, the linear predictor may be positive, which would give an impossible negative mean. When maximizing the likelihood, precautions must be taken to avoid this. An alternative is to use a noncanonical link function.

In the case of the Bernoulli, binomial, categorical and multinomial distributions, the support of the distributions is not the same type of data as the parameter being predicted. In all of these cases, the predicted parameter is one or more probabilities, i.e. real numbers in the range . The resulting model is known as * logistic regression * (or * multinomial logistic regression * in the case that K-way rather than binary values are being predicted).

For the Bernoulli and binomial distributions, the parameter is a single probability, indicating the likelihood of occurrence of a single event. The Bernoulli still satisfies the basic condition of the generalized linear model in that, even though a single outcome will always be either 0 or 1, the * expected value * will nonetheless be a real-valued probability, i.e. the probability of occurrence of a "yes" (or 1) outcome. Similarly, in a binomial distribution, the expected value is *Np*, i.e. the expected proportion of "yes" outcomes will be the probability to be predicted.

For categorical and multinomial distributions, the parameter to be predicted is a *K*-vector of probabilities, with the further restriction that all probabilities must add up to 1. Each probability indicates the likelihood of occurrence of one of the *K* possible values. For the multinomial distribution, and for the vector form of the categorical distribution, the expected values of the elements of the vector can be related to the predicted probabilities similarly to the binomial and Bernoulli distributions.

The maximum likelihood estimates can be found using an iteratively reweighted least squares algorithm or a Newton's method with updates of the form:

where is the observed information matrix (the negative of the Hessian matrix) and is the score function; or a Fisher's scoring method:

where is the Fisher information matrix. Note that if the canonical link function is used, then they are the same.^{ [3] }

In general, the posterior distribution cannot be found in closed form and so must be approximated, usually using Laplace approximations or some type of Markov chain Monte Carlo method such as Gibbs sampling.

A possible point of confusion has to do with the distinction between generalized linear models and general linear models, two broad statistical models. Co-originator John Nelder has expressed regret over this terminology.^{ [4] }

The general linear model may be viewed as a special case of the generalized linear model with identity link and responses normally distributed. As most exact results of interest are obtained only for the general linear model, the general linear model has undergone a somewhat longer historical development. Results for the generalized linear model with non-identity link are asymptotic (tending to work well with large samples).

A simple, very important example of a generalized linear model (also an example of a general linear model) is linear regression. In linear regression, the use of the least-squares estimator is justified by the Gauss–Markov theorem, which does not assume that the distribution is normal.

From the perspective of generalized linear models, however, it is useful to suppose that the distribution function is the normal distribution with constant variance and the link function is the identity, which is the canonical link if the variance is known. Under these assumptions, the least-squares estimator is obtained as the maximum-likelihood parameter estimate.

For the normal distribution, the generalized linear model has a closed form expression for the maximum-likelihood estimates, which is convenient. Most other GLMs lack closed form estimates.

When the response data, *Y*, are binary (taking on only values 0 and 1), the distribution function is generally chosen to be the Bernoulli distribution and the interpretation of *μ*_{i} is then the probability, *p*, of *Y*_{i} taking on the value one.

There are several popular link functions for binomial functions.

The most typical link function is the canonical logit link:

GLMs with this setup are logistic regression models (or *logit models*).

Alternatively, the inverse of any continuous cumulative distribution function (CDF) can be used for the link since the CDF's range is , the range of the binomial mean. The normal CDF is a popular choice and yields the probit model. Its link is

The reason for the use of the probit model is that a constant scaling of the input variable to a normal CDF (which can be absorbed through equivalent scaling of all of the parameters) yields a function that is practically identical to the logit function, but probit models are more tractable in some situations than logit models. (In a Bayesian setting in which normally distributed prior distributions are placed on the parameters, the relationship between the normal priors and the normal CDF link function means that a probit model can be computed using Gibbs sampling, while a logit model generally cannot.)

The complementary log-log function may also be used:

This link function is asymmetric and will often produce different results from the logit and probit link functions.^{ [5] } The cloglog model corresponds to applications where we observe either zero events (e.g., defects) or one or more, where the number of events is assumed to follow the Poisson distribution.^{ [6] } The Poisson assumption means that

where *μ* is a positive number denoting the expected number of events. If *p* represents the proportion of observations with at least one event, its complement

and then

A linear model requires the response variable to take values over the entire real line. Since *μ* must be positive, we can enforce that by taking the logarithm, and letting log(*μ*) be a linear model. This produces the "cloglog" transformation

The identity link *g(p) = p* is also sometimes used for binomial data to yield a linear probability model. However, the identity link can predict nonsense "probabilities" less than zero or greater than one. This can be avoided by using a transformation like cloglog, probit or logit (or any inverse cumulative distribution function). A primary merit of the identity link is that it can be estimated using linear math—and other standard link functions are approximately linear matching the identity link near *p* = 0.5.

The variance function for "quasibinomial" data is:

where the dispersion parameter *τ* is exactly 1 for the binomial distribution. Indeed, the standard binomial likelihood omits *τ*. When it is present, the model is called "quasibinomial", and the modified likelihood is called a quasi-likelihood, since it is not generally the likelihood corresponding to any real family of probability distributions. If *τ* exceeds 1, the model is said to exhibit overdispersion.

The binomial case may be easily extended to allow for a multinomial distribution as the response (also, a Generalized Linear Model for counts, with a constrained total). There are two ways in which this is not usually done:

If the response variable is ordinal, then one may fit a model function of the form:

for *m* > 2. Different links *g* lead to ordinal regression models like proportional odds models or ordered probit models.

If the response variable is a nominal measurement, or the data do not satisfy the assumptions of an ordered model, one may fit a model of the following form:

for *m* > 2. Different links *g* lead to multinomial logit or multinomial probit models. These are more general than the ordered response models, and more parameters are estimated.

Another example of generalized linear models includes Poisson regression which models count data using the Poisson distribution. The link is typically the logarithm, the canonical link.

The variance function is proportional to the mean

where the dispersion parameter *τ* is typically fixed at exactly one. When it is not, the resulting quasi-likelihood model is often described as Poisson with overdispersion or *quasi-Poisson*.

The standard GLM assumes that the observations are uncorrelated. Extensions have been developed to allow for correlation between observations, as occurs for example in longitudinal studies and clustered designs:

**Generalized estimating equations**(GEEs) allow for the correlation between observations without the use of an explicit probability model for the origin of the correlations, so there is no explicit likelihood. They are suitable when the random effects and their variances are not of inherent interest, as they allow for the correlation without explaining its origin. The focus is on estimating the average response over the population ("population-averaged" effects) rather than the regression parameters that would enable prediction of the effect of changing one or more components of**X**on a given individual. GEEs are usually used in conjunction with Huber–White standard errors.^{ [7] }^{ [8] }**Generalized linear mixed models**(GLMMs) are an extension to GLMs that includes random effects in the linear predictor, giving an explicit probability model that explains the origin of the correlations. The resulting "subject-specific" parameter estimates are suitable when the focus is on estimating the effect of changing one or more components of**X**on a given individual. GLMMs are also referred to as multilevel models and as mixed model. In general, fitting GLMMs is more computationally complex and intensive than fitting GEEs.

Generalized additive models (GAMs) are another extension to GLMs in which the linear predictor *η* is not restricted to be linear in the covariates **X** but is the sum of smoothing functions applied to the *x _{i}*s:

The smoothing functions *f _{i}* are estimated from the data. In general this requires a large number of data points and is computationally intensive.

The **likelihood function** describes the joint probability of the observed data as a function of the parameters of the chosen statistical model. For each specific parameter value in the parameter space, the likelihood function therefore assigns a probabilistic prediction to the observed data . Since it is essentially the product of sampling densities, the likelihood generally encapsulates both the data-generating process as well as the missing-data mechanism that produced the observed sample.

In statistics, the (binary) **logistic model** is a statistical model that models the probability of one event taking place by having the log-odds for the event be a linear combination of one or more independent variables ("predictors"). In regression analysis, **logistic regression** is estimating the parameters of a logistic model. Formally, in binary logistic regression there is a single binary dependent variable, coded by a indicator variable, where the two values are labeled "0" and "1", while the independent variables can each be a binary variable or a continuous variable. The corresponding probability of the value labeled "1" can vary between 0 and 1, hence the labeling; the function that converts log-odds to probability is the logistic function, hence the name. The unit of measurement for the log-odds scale is called a *logit*, from * logistic unit*, hence the alternative names. See § Background and § Definition for formal mathematics, and § Example for a worked example.

In probability and statistics, an **exponential family** is a parametric set of probability distributions of a certain form, specified below. This special form is chosen for mathematical convenience, based on some useful algebraic properties, as well as for generality, as exponential families are in a sense very natural sets of distributions to consider. The term **exponential class** is sometimes used in place of "exponential family", or the older term **Koopman–Darmois family**. The terms "distribution" and "family" are often used loosely: specifically, *an* exponential family is a *set* of distributions, where the specific distribution varies with the parameter; however, a parametric *family* of distributions is often referred to as "*a* distribution", and the set of all exponential families is sometimes loosely referred to as "the" exponential family. They are distinct because they possess a variety of desirable properties, most importantly the existence of a sufficient statistic.

In statistics, **deviance** is a goodness-of-fit statistic for a statistical model; it is often used for statistical hypothesis testing. It is a generalization of the idea of using the sum of squares of residuals (RSS) in ordinary least squares to cases where model-fitting is achieved by maximum likelihood. It plays an important role in exponential dispersion models and generalized linear models.

In statistics, an **expectation–maximization** (**EM**) **algorithm** is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the *E* step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.

In statistics, a **probit model** is a type of regression where the dependent variable can take only two values, for example married or not married. The word is a portmanteau, coming from * probability* +

In statistics, **Poisson regression** is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes the response variable *Y* has a Poisson distribution, and assumes the logarithm of its expected value can be modeled by a linear combination of unknown parameters. A Poisson regression model is sometimes known as a log-linear model, especially when used to model contingency tables.

In statistics, **multinomial logistic regression** is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes. That is, it is a model that is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables.

In statistics, **binomial regression** is a regression analysis technique in which the response has a binomial distribution: it is the number of successes in a series of independent Bernoulli trials, where each trial has probability of success . In binomial regression, the probability of a success is related to explanatory variables: the corresponding concept in ordinary regression is to relate the mean value of the unobserved response to explanatory variables.

In statistics, **Bayesian linear regression** is an approach to linear regression in which the statistical analysis is undertaken within the context of Bayesian inference. When the regression model has errors that have a normal distribution, and if a particular form of prior distribution is assumed, explicit results are available for the posterior probability distributions of the model's parameters.

In probability and statistics, a **natural exponential family** (**NEF**) is a class of probability distributions that is a special case of an exponential family (EF).

In probability and statistics, the class of **exponential dispersion models** (**EDM**) is a set of probability distributions that represents a generalisation of the natural exponential family. Exponential dispersion models play an important role in statistical theory, in particular in generalized linear models because they have a special structure which enables deductions to be made about appropriate statistical inference.

The **shifted log-logistic distribution** is a probability distribution also known as the **generalized log-logistic** or the **three-parameter log-logistic** distribution. It has also been called the **generalized logistic** distribution, but this conflicts with other uses of the term: see generalized logistic distribution.

In statistics, **ordinal regression**, also called **ordinal classification**, is a type of regression analysis used for predicting an ordinal variable, i.e. a variable whose value exists on an arbitrary scale where only the relative ordering between different values is significant. It can be considered an intermediate problem between regression and classification. Examples of ordinal regression are ordered logit and ordered probit. Ordinal regression turns up often in the social sciences, for example in the modeling of human levels of preference, as well as in information retrieval. In machine learning, ordinal regression may also be called **ranking learning**.

In statistics, **hierarchical generalized linear models** extend generalized linear models by relaxing the assumption that error components are independent. This allows models to be built in situations where more than one error term is necessary and also allows for dependencies between error terms. The error components can be correlated and not necessarily follow a normal distribution. When there are different clusters, that is, groups of observations, the observations in the same cluster are correlated. In fact, they are positively correlated because observations in the same cluster share some common features. In this situation, using generalized linear models and ignoring the correlations may cause problems.

In statistics, the **variance function** is a smooth function which depicts the variance of a random quantity as a function of its mean. The variance function is a measure of heteroscedasticity and plays a large role in many settings of statistical modelling. It is a main ingredient in the generalized linear model framework and a tool used in non-parametric regression, semiparametric regression and functional data analysis. In parametric modeling, variance functions take on a parametric form and explicitly describe the relationship between the variance and the mean of a random quantity. In a non-parametric setting, the variance function is assumed to be a smooth function.

The **generalized functional linear model** (**GFLM**) is an extension of the generalized linear model (GLM) that allows one to regress univariate responses of various types on functional predictors, which are mostly random trajectories generated by a square-integrable stochastic processes. Similarly to GLM, a link function relates the expected value of the response variable to a linear predictor, which in case of GFLM is obtained by forming the scalar product of the random predictor function with a smooth parameter function . Functional Linear Regression, Functional Poisson Regression and Functional Binomial Regression, with the important Functional Logistic Regression included, are special cases of GFLM. Applications of GFLM include classification and discrimination of stochastic processes and functional data.

In statistics, **linear regression** is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables. The case of one explanatory variable is called *simple linear regression*; for more than one, the process is called **multiple linear regression**. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

In statistics, the class of **vector generalized linear models** (**VGLMs**) was proposed to enlarge the scope of models catered for by generalized linear models (**GLMs**). In particular, VGLMs allow for response variables outside the classical exponential family and for more than one parameter. Each parameter can be transformed by a *link function*. The VGLM framework is also large enough to naturally accommodate multiple responses; these are several independent responses each coming from a particular statistical distribution with possibly different parameter values.

The **hyperbolastic functions**, also known as **hyperbolastic growth models**, are mathematical functions that are used in medical statistical modeling. These models were originally developed to capture the growth dynamics of multicellular tumor spheres, and were introduced in 2005 by Mohammad Tabatabai, David Williams, and Zoran Bursac. The precision of hyperbolastic functions in modeling real world problems is somewhat due to their flexibility in their point of inflection. These functions can be used in a wide variety of modeling problems such as tumor growth, stem cell proliferation, pharma kinetics, cancer growth, sigmoid activation function in neural networks, and epidemiological disease progression or regression.

- ↑ Nelder, John; Wedderburn, Robert (1972). "Generalized Linear Models".
*Journal of the Royal Statistical Society. Series A (General)*. Blackwell Publishing.**135**(3): 370–384. doi:10.2307/2344614. JSTOR 2344614. S2CID 14154576. - ↑ McCullagh & Nelder 1989, Chapter 2.
- ↑ McCullagh & Nelder 1989, p. 43.
- ↑ Senn, Stephen (2003). "A conversation with John Nelder".
*Statistical Science*.**18**(1): 118–131. doi: 10.1214/ss/1056397489 .I suspect we should have found some more fancy name for it that would have stuck and not been confused with the general linear model, although general and generalized are not quite the same. I can see why it might have been better to have thought of something else.

- ↑ "Complementary Log-log Model" (PDF).
- ↑ "Which Link Function — Logit, Probit, or Cloglog?".
*Bayesium Analytics*. 2015-08-14. Retrieved 2019-03-17. - ↑ Zeger, Scott L.; Liang, Kung-Yee; Albert, Paul S. (1988). "Models for Longitudinal Data: A Generalized Estimating Equation Approach".
*Biometrics*. International Biometric Society.**44**(4): 1049–1060. doi:10.2307/2531734. JSTOR 2531734. PMID 3233245. - ↑ Hardin, James; Hilbe, Joseph (2003).
*Generalized Estimating Equations*. London, England: Chapman and Hall/CRC. ISBN 1-58488-307-3. - ↑ Hastie & Tibshirani 1990.
- ↑ Wood 2006.

- Hastie, T. J.; Tibshirani, R. J. (1990).
*Generalized Additive Models*. Chapman & Hall/CRC. ISBN 978-0-412-34390-2. - Madsen, Henrik; Thyregod, Poul (2011).
*Introduction to General and Generalized Linear Models*. Chapman & Hall/CRCC. ISBN 978-1-4200-9155-7. - McCullagh, Peter; Nelder, John (1989).
*Generalized Linear Models*(2nd ed.). Boca Raton, FL: Chapman and Hall/CRC. ISBN 0-412-31760-5. - Wood, Simon (2006).
*Generalized Additive Models: An Introduction with R*. Chapman & Hall/CRC. ISBN 1-58488-474-6.

- Dunn, P.K.; Smyth, G.K. (2018).
*Generalized Linear Models With Examples in R*. New York: Springer. doi:10.1007/978-1-4419-0118-7. ISBN 978-1-4419-0118-7. - Dobson, A.J.; Barnett, A.G. (2008).
*Introduction to Generalized Linear Models*(3rd ed.). Boca Raton, FL: Chapman and Hall/CRC. ISBN 978-1-58488-165-0. - Hardin, James; Hilbe, Joseph (2007).
*Generalized Linear Models and Extensions*(2nd ed.). College Station: Stata Press. ISBN 978-1-59718-014-6.

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