Natural exponential family

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). Every distribution possessing a moment-generating function is a member of a natural exponential family, and the use of such distributions simplifies the theory and computation of generalized linear models.

Definition

Probability distribution function (PDF) of the univariate case (scalar domain, scalar parameter)

The natural exponential family (NEF) is a subset of the exponential family. NEF is an exponential family in which the natural parameter η and the natural statistic T(x) are both the identity. A distribution in the exponential family with parameter θ can be written with probability density function (PDF)

f_{X}(x|\theta )=h(x)\ \exp {\Big (}\ \eta (\theta )T(x)-A(\theta )\ {\Big )}\,\!,

where $h(x)$ and $A(\theta )$ are known functions. A distribution in the natural exponential family with parameter θ can thus be written with PDF

f_{X}(x|\theta )=h(x)\ \exp {\Big (}\ \theta x-A(\theta )\ {\Big )}\,\!.

[Note that slightly different notation is used by the originator of the NEF, Carl Morris.^[1] Morris uses ω instead of η and ψ instead of A.]

Probability distribution function (PDF) of the general case (multivariate domain and/or parameter)

Suppose that $\mathbf {x} \in {\mathcal {X}}\subseteq \mathbb {R} ^{p}$ , then a natural exponential family of order p has density or mass function of the form:

f_{X}(\mathbf {x} |{\boldsymbol {\theta }})=h(\mathbf {x} )\ \exp {\Big (}{\boldsymbol {\theta }}^{\rm {T}}\mathbf {x} -A({\boldsymbol {\theta }})\ {\Big )}\,\!,

where in this case the parameter ${\boldsymbol {\theta }}\in \mathbb {R} ^{p}.$

Moment and cumulant generating function

A member of a natural exponential family has moment generating function (MGF) of the form

M_{X}(\mathbf {t} )=\exp {\Big (}\ A({\boldsymbol {\theta }}+\mathbf {t} )-A({\boldsymbol {\theta }})\ {\Big )}\,.

The cumulant generating function is by definition the logarithm of the MGF, so it is

K_{X}(\mathbf {t} )=A({\boldsymbol {\theta }}+\mathbf {t} )-A({\boldsymbol {\theta }})\,.

Examples

The five most important univariate cases are:

normal distribution with known variance
Poisson distribution
gamma distribution with known shape parameter α (or k depending on notation set used)
binomial distribution with known number of trials, n
negative binomial distribution with known $r$

These five examples – Poisson, binomial, negative binomial, normal, and gamma – are a special subset of NEF, called NEF with quadratic variance function (NEF-QVF) because the variance can be written as a quadratic function of the mean. NEF-QVF are discussed below.

Distributions such as the exponential, chi-squared, Rayleigh, Weibull, Bernoulli, and geometric distributions are special cases of the above five distributions. Many common distributions are either NEF or can be related to the NEF. For example: the chi-squared distribution is a special case of the gamma distribution. The Bernoulli distribution is a binomial distribution with n = 1 trial. The exponential distribution is a gamma distribution with shape parameter α = 1 (or k = 1 ). The Rayleigh and Weibull distributions can each be written in terms of an exponential distribution.

Some exponential family distributions are not NEF. The lognormal and Beta distribution are in the exponential family, but not the natural exponential family.

The parameterization of most of the above distributions has been written differently from the parameterization commonly used in textbooks and the above linked pages. For example, the above parameterization differs from the parameterization in the linked article in the Poisson case. The two parameterizations are related by $\theta =\log(\lambda )$ , where λ is the mean parameter, and so that the density may be written as

f(k;\theta )={\frac {1}{k!}}\exp {\Big (}\ \theta \ k-\exp(\theta )\ {\Big )}\ ,

for $\theta \in \mathbb {R}$ , so

h(k)={\frac {1}{k!}}\

, and

A(\theta )=\exp(\theta )\ .

This alternative parameterization can greatly simplify calculations in mathematical statistics. For example, in Bayesian inference, a posterior probability distribution is calculated as the product of two distributions. Normally this calculation requires writing out the probability distribution functions (PDF) and integrating; with the above parameterization, however, that calculation can be avoided. Instead, relationships between distributions can be abstracted due to the properties of the NEF described below.

An example of the multivariate case is the multinomial distribution with known number of trials.

Properties

The properties of the natural exponential family can be used to simplify calculations involving these distributions.

Univariant case

1. The cumulants of an NEF can be calculated as derivatives of the NEF's cumulant generating function. The nth cumulant is the nth derivative with respect to θ of the cumulant generating function.

The cumulant generating function is

K_{X}(t)=A(\theta +t)-A(\theta )\,.

The first cumulant is

K_{1}={\frac {d}{d\theta }}A(t)\,.

The mean is the first moment and always equal to the first cumulant, so

\mu _{1}'=\kappa _{1}=\mathrm {E} [X]=K'_{X}(0)=A'(\theta )\,.

The variance is always the second moment, and it is always related to the first and second cumulants by

\mathrm {Var} [X]=\mu _{2}'=\kappa _{2}+\kappa _{1}^{2}\,,

so that

\mathrm {Var} [X]=K''_{X}(0)=A''(\theta )\,.

The nth cumulant is

K_{n}={\frac {d^{(n)}}{d\theta ^{(n)}}}A(t)\,.

2. Natural exponential families (NEF) are closed under convolution.

Given independent identically distributed (iid) $X_{1},\ldots ,X_{n}$ with distribution from an NEF, then $\sum _{i=1}^{n}X_{i}\,$ is an NEF, although not necessarily the original NEF. This follows from the properties of the cumulant generating function.

3. The variance function for random variables with an NEF distribution can be written in terms of the mean.

Var(X)=V(\mu ).

4. The first two moments of a NEF distribution uniquely specify the distribution within that family of distributions.

X\sim NEF[\mu ,V(\mu )].

Multivariate case

In the multivariate case, the mean vector and covariance matrix are

\mathrm {E} [X]=\nabla A({\boldsymbol {\theta }})\,

and

\mathrm {Cov} [X]=\nabla \nabla ^{\rm {T}}A({\boldsymbol {\theta }})\,,

where $\nabla$ is the gradient and $\nabla \nabla ^{\rm {T}}$ is the Hessian matrix.

Natural exponential families with quadratic variance functions (NEF-QVF)

A special case of the natural exponential families are those with quadratic variance functions. Six NEFs have quadratic variance functions (QVF) in which the variance of the distribution can be written as a quadratic function of the mean. These are called NEF-QVF. The properties of these distributions were first described by Carl Morris.^[2]

Var(X)=V(\mu )=\nu _{0}+\nu _{1}\mu +\nu _{2}\mu ^{2}.

The six NEF-QVFs

The six NEF-QVF are written here in increasing complexity of the relationship between variance and mean.

1. The normal distribution with fixed variance $X\,\sim N(\mu ,\sigma ^{2})$ is NEF-QVF because the variance is constant. The variance can be written $Var(X)=V(\mu )=\sigma ^{2}$ , so variance is a degree 0 function of the mean.

2. The Poisson distribution $X\,\sim Pois(\mu )$ is NEF-QVF because all Poisson distributions have variance equal to the mean $Var(X)=V(\mu )=\mu$ , so variance is a linear function of the mean.

3. The Gamma distribution $X\,\sim Gam(r,\lambda )$ is NEF-QVF because the mean of the Gamma distribution is $\mu =r\lambda$ and the variance of the Gamma distribution is $Var(X)=V(\mu )=\mu ^{2}/r$ , so the variance is a quadratic function of the mean.

4. The binomial distribution $X\,\sim Bin(n,p)$ is NEF-QVF because the mean is $\mu =np$ and the variance is $Var(X)=np(1-p)$ which can be written in terms of the mean as $V(X)=-np^{2}+np=-\mu ^{2}/n+\mu .$

5. The negative binomial distribution $X\sim NegBin(n,p)$ is NEF-QVF because the mean is $\mu =np/(1-p)$ and the variance is $V(\mu )=\mu ^{2}/n+\mu .$

6. The (not very famous) distribution generated by the generalized hyperbolic secant distribution (NEF-GHS) has $V(\mu )=\mu ^{2}/n+n$ and $\mu >0.$

Properties of NEF-QVF

The properties of NEF-QVF can simplify calculations that use these distributions.

1. Natural exponential families with quadratic variance functions (NEF-QVF) are closed under convolutions of a linear transformation. That is, a convolution of a linear transformation of an NEF-QVF is also an NEF-QVF, although not necessarily the original one.

Given independent identically distributed (iid) $X_{1},\ldots ,X_{n}$ with distribution from a NEF-QVF. A convolution of a linear transformation of an NEF-QVF is also an NEF-QVF.

Let $Y=\sum _{i=1}^{n}(X_{i}-b)/c\,$ be the convolution of a linear transformation of X. The mean of Y is $\mu ^{*}=n(\mu -b)/c\,$ . The variance of Y can be written in terms of the variance function of the original NEF-QVF. If the original NEF-QVF had variance function

Var(X)=V(\mu )=\nu _{0}+\nu _{1}\mu +\nu _{2}\mu ^{2},

then the new NEF-QVF has variance function

Var(Y)=V^{*}(\mu ^{*})=\nu _{0}^{*}+\nu _{1}^{*}\mu +\nu _{2}^{*}\mu ^{2},

where

\nu _{0}^{*}=nV(b)/c^{2}\,,

\nu _{1}^{*}=V'(b)/c\,,

\nu _{2}^{*}/n=\nu _{2}/n\,.

2. Let $X_{1}$ and $X_{2}$ be independent NEF with the same parameter θ and let $Y=X_{1}+X_{2}$ . Then the conditional distribution of $X_{1}$ given Y $f(X_{1}|Y)$ has quadratic variance in Y if and only if $X_{1}$ and $X_{2}$ are NEF-QVF. Examples of conditional distributions $f(X_{1}|Y)$ are the normal, binomial, beta, hypergeometric and geometric distributions, which are not all NEF-QVF.^[1]

3. NEF-QVF have conjugate prior distributions on μ in the Pearson system of distributions (also called the Pearson distribution although the Pearson system of distributions is actually a family of distributions rather than a single distribution.) Examples of conjugate prior distributions of NEF-QVF distributions are the normal, gamma, reciprocal gamma, beta, F-, and t- distributions. Again, these conjugate priors are not all NEF-QVF.^[1]

4. If $X|\mu$ has an NEF-QVF distribution and μ has a conjugate prior distribution then the marginal distributions are well-known distributions.^[1]

These properties together with the above notation can simplify calculations in mathematical statistics that would normally be done using complicated calculations and calculus.

References

1 2 3 4 Morris C. (2006) "Natural exponential families", Encyclopedia of Statistical Sciences.
↑ Morris C. (1982) "Natural exponential families with quadratic variance functions". Ann. Stat., 10(1), 65–80.

Morris C. (1982) Natural exponential families with quadratic variance functions: statistical theory. Dept of mathematics, Institute of Statistics, University of Texas, Austin.

Probability distributions

List

Discrete univariate with finite support	Benford Bernoulli beta-binomial binomial categorical hypergeometric Poisson binomial Rademacher discrete uniform Zipf Zipf–Mandelbrot

Discrete univariate with infinite support	beta negative binomial Borel Conway–Maxwell–Poisson discrete phase-type Delaporte extended negative binomial Gauss–Kuzmin geometric logarithmic negative binomial parabolic fractal Poisson Skellam Yule–Simon zeta

Continuous univariate supported on a bounded interval	arcsine ARGUS Balding–Nichols Bates beta beta rectangular Irwin–Hall Kumaraswamy logit-normal noncentral beta raised cosine reciprocal triangular U-quadratic uniform Wigner semicircle

Continuous univariate supported on a semi-infinite interval	Benini Benktander 1st kind Benktander 2nd kind beta prime Burr chi-squared chi Dagum Davis exponential-logarithmic Erlang exponential F folded normal Flory–Schulz Fréchet gamma gamma/Gompertz generalized inverse Gaussian Gompertz half-logistic half-normal Hotelling's T-squared hyper-Erlang hyperexponential hypoexponential inverse chi-squared scaled inverse chi-squared inverse Gaussian inverse gamma Kolmogorov Lévy log-Cauchy log-Laplace log-logistic log-normal Lomax matrix-exponential Maxwell–Boltzmann Maxwell–Jüttner Mittag-Leffler Nakagami noncentral chi-squared Pareto phase-type poly-Weibull Rayleigh relativistic Breit–Wigner Rice shifted Gompertz truncated normal type-2 Gumbel Weibull Discrete Weibull Wilks's lambda

Continuous univariate supported on the whole real line	Cauchy exponential power Fisher's z Gaussian q generalized normal generalized hyperbolic geometric stable Gumbel Holtsmark hyperbolic secant Johnson's S_U Landau Laplace asymmetric Laplace logistic noncentral t normal (Gaussian) normal-inverse Gaussian skew normal slash stable Student's t type-1 Gumbel Tracy–Widom variance-gamma Voigt

Continuous univariate with support whose type varies	generalized extreme value generalized Pareto Tukey lambda q-Gaussian q-exponential q-Weibull shifted log-logistic

Mixed continuous-discrete univariate	rectified Gaussian

Multivariate (joint)	Discrete Ewens multinomial Dirichlet-multinomial negative multinomial Continuous Dirichlet generalized Dirichlet multivariate normal multivariate stable multivariate t normal-inverse-gamma normal-gamma Matrix-valued inverse matrix gamma inverse-Wishart matrix normal matrix t matrix gamma normal-inverse-Wishart normal-Wishart Wishart

Directional	Univariate (circular) directional Circular uniform univariate von Mises wrapped normal wrapped Cauchy wrapped exponential wrapped asymmetric Laplace wrapped Lévy Bivariate (spherical) Kent Bivariate (toroidal) bivariate von Mises Multivariate von Mises–Fisher Bingham

Degenerate and singular	Degenerate Dirac delta function Singular Cantor

Families	Circular compound Poisson elliptical exponential natural exponential location-scale maximum entropy mixture Pearson Tweedie wrapped

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