In the generalised additive (mixed) model R package mgcv [40] the univariate function estimates use a further variant of penalised splines – low-rank thin-plate splines [39]. η ) Generally, this means that all of the factors constituting the density or mass function must be of one of the following forms: where f and h are arbitrary functions of x; g and j are arbitrary functions of θ; and c is an arbitrary "constant" expression (i.e. x Estimation of parameters is revisited in two-parameter exponential distributions. We illustrate using the simple case of a one-dimensional parameter, but an analogous derivation holds more generally. Nothing really changes except t(x) has changed to Tt(x). ν 2 i k {\displaystyle P_{A,\theta }} x x from Consider now a collection of observable quantities (random variables) Ti. a normal distribution with a known mean is in the one parameter Exponential family, while a normal distribution with both parameters unknown is in the two parameter Exponential family. {\displaystyle {\rm {d\,}}H(\mathbf {x} )} where T(x), h(x), η(θ), and A(θ) are known functions. Suppose H is a non-decreasing function of a real variable. As in Example 6.9.2, reparametrize and transform the data as: To put the problem in the framework of a two-parameter exponential family, we further reparametrize: The UMP unbiased level α test for H0: θ = θ0 vs H1: θ≠θ0 (τ being a nuisance parameter) is. (equivalently, the number of parameters of the distribution of a single data point). 3 p Similarly. . η ⁡ f e + log + Additional information about these distributions as well as other continuous distributions can be found in Johnson et al. ⁡ , T The entropy of dF(x) relative to dH(x) is, where dF/dH and dH/dF are Radon–Nikodym derivatives. We start with the normalization of the probability distribution. , x ) x The above forms may sometimes be seen with {\displaystyle \mathbf {X} =(x_{1},\ldots ,x_{n})} The data X enters into this equation only in the expression. − = First, assume that the probability of a single observation follows an exponential family, parameterized using its natural parameter: Then, for data θ This makes the computation of the posterior particularly simple. Many of the distributions in the exponential family are standard, workhorse distributions in statistics, w/ convenient statistical properties. While direct and fair comparison between MCMC and VB is difficult in sacrificing some accuracy the VB approach is often orders of magnitude faster than MCMC methods. We prove that there exists a piv otal quantity, as a function of a complete su cient statistic, with a chi-square distribution. 1 The following table shows how to rewrite a number of common distributions as exponential-family distributions with natural parameters. When the reference measure is finite, it can be normalized and H is actually the cumulative distribution function of a probability distribution. η In this case, H is also absolutely continuous and can be written Confidence interval for the scale parameter and predictive interval for a future independent observation have been studied by many, including Petropoulos (2011) and Lawless (1977), respectively. A and B are events in a probability space. ( ∑ − log The gradient statistic takes the form ST=n(ϕ0−ϕ^)α′(ϕ0)[β(ϕ0)+d-], where d-=n−1∑l=1nd(xl). V Because of the way that the sufficient statistic is computed, it necessarily involves sums of components of the data (in some cases disguised as products or other forms — a product can be written in terms of a sum of logarithms). , {\displaystyle A({\boldsymbol {\eta }})} The dimension k of the random variable need not match the dimension d of the parameter vector, nor (in the case of a curved exponential function) the dimension s of the natural parameter ⋅ Let X∼Binomialm,π1 and Y∼Binomialn,π2 be independent. First, for η1: Where Examples: The normal distribution , N ⁢ ( μ , σ 2 ) , treating σ 2 as a nuisance parameter, belongs to the exponential family. n d x 1 A i 2 {\displaystyle {\boldsymbol {\theta }}.}. 2 and again factorizes inside of the exponent. . In standard exponential families, the derivatives of this function correspond to the moments (more technically, the cumulants) of the sufficient statistics, e.g. As a counterexample if these conditions are relaxed, the family of uniform distributions (either discrete or continuous, with either or both bounds unknown) has a sufficient statistic, namely the sample maximum, sample minimum, and sample size, but does not form an exponential family, as the domain varies with the parameters. ( [ In Mathematics in Science and Engineering, 2005. {\displaystyle {\boldsymbol {\eta }}^{\mathsf {T}}\mathbf {T} (x)} Penalised splines form the foundation of semiparametric regression models and include, as special cases, smoothing splines (e.g. The reason for this is so that the moments of the sufficient statistics can be calculated easily, simply by differentiating this function. The Beta family of distributions are conjugate priors for the Binomial family of distributions and so are often used for Bayesian inference. ( The relation between the latter and the former is: To convert between the representations involving the two types of parameter, use the formulas below for writing one type of parameter in terms of the other. θ {\displaystyle \exp \! | The parameter space is R×R+ where R+=x:x>0 and the pdf is, Gamma (α, β). x η x Example 3.2 (One-parameter exponential family)Let x1,…,xn be n independent observations in which each xl has a distribution in the one-parameter exponential family with probability density function f(x;ϕ)=1ξ(ϕ)exp[−α(ϕ)d(x)+v(x)],where α(⋅), v(⋅), d(⋅) and ξ(⋅) are known functions, and β(ϕ) = ξ′(ϕ)/(ξ(ϕ)α′(ϕ)). The family of Pareto distributions with a fixed minimum bound xm form an exponential family. η 2 2 ⁡ ( ] We can find the mean of the sufficient statistics as follows. η More generally, η(θ) and T(x) can each be vector-valued such that Can a two parameter Weibull Distribution be written as an exponential family form? 2 H Following are some detailed examples of the representation of some useful distribution as exponential families. Artur J. Lemonte, in The Gradient Test, 2016, We initially assume that the model is indexed by a scalar unknown parameter, say ϕ. m being the scale parameter) and its support, therefore, has a lower limit of Additional applications come from the fact that the exponential distribution and chi-squared distributions are special cases of the Gamma distribution. 1 Using Equation (4.13) characterize all independent subfamilies. 2 . Often x is a vector of measurements, in which case T(x) may be a function from the space of possible values of x to the real numbers. {\displaystyle \,{\rm {d\,}}F(x)=f(x)~{\rm {d\,}}x\,} ( ) {\displaystyle p_{i}} ) In such a case, all values of θ mapping to the same η(θ) will also have the same value for A(θ) and g(θ). = A conjugate prior is one which, when combined with the likelihood and normalised, produces a posterior distribution which is of the same type as the prior.   The Bartlett-corrected gradient statistic takes the form. f The interest lies in testing the null hypothesis H0:ϕ=ϕ0 against Ha:ϕ≠ϕ0, where ϕ0 is a fixed value. | P For example, Lawless 1 applied the two-parameter exponential distribution to analyze lifetime data, and Baten and Kamil 2 applied the distribution to This technique is often useful when T is a complicated function of the data, whose moments are difficult to calculate by integration. and sufficient statistic T(x) . two or more different values of θ map to the same value of η(θ), and hence η(θ) cannot be inverted. which is termed the sufficient statistic of the data. i Both of these expectations are needed when deriving the variational Bayes update equations in a Bayes network involving a Wishart distribution (which is the conjugate prior of the multivariate normal distribution). 1 η We have that κϕϕ = −α′β′, κϕϕϕ = −(2α″β′ + α′β″), and κϕϕϕϕ = −3α″β″ − 3α‴β′− α′β‴. 2 η Here, A1 = 12, A2 = 15, A3 = 5, E(ST)=1+1/n, VAR(ST)=2+9/n, μ3(ST) = 8 + 94/n, and. If φ is known, this is a one-parameter exponential family with θ being the canonical parameter . 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. F the scale parameter of the exponential family of distributions. In probability theory and statistics, the gamma distribution is a two-parameter family of continuous probability distributions.The exponential distribution, Erlang distribution, and chi-square distribution are special cases of the gamma distribution. Less tersely, suppose Xk, (where k = 1, 2, 3, ... n) are independent, identically distributed random variables. Typically VB methods underestimate posterior variances, and as such, their use in the context of inference is sometimes questionable. η Applications of the Gamma distribution appear in many fields including insurance claims and genetics. , stopping-time parameter) r is an exponential family. T x ⁡ ≥ The function A important in its own right, because the mean, variance and other moments of the sufficient statistic T(x) can be derived simply by differentiating A(η). Examples are typical Gaussian mixture models as well as many heavy-tailed distributions that result from compounding (i.e. H(x) is a Lebesgue–Stieltjes integrator for the reference measure. = T The multinomial pmf of (X, Y, Z) can now be rewritten, using Y = S − X, Z = T − X and the above formulas for pAB, pABc, pAcB, and pAcBc to express the pmf of (X, S, T) as: Now the UMP unbiased level α test for H0: Δ ≥ 1 vs H1: Δ < 1 (ie, for H0: θ ≤ 0 vs H1: θ > 0) is given by. The posterior will then have to be computed by numerical methods. Next, consider the case of a normal distribution with unknown mean and unknown variance. η We describe some geometric results relating the two. {\displaystyle +\log \Gamma _{p}\left(-{\Big (}\eta _{2}+{\frac {p+1}{2}}{\Big )}\right)}, − ( η Laplace (θ > 0, k ∈ ℝ, k known, x ∈ ℝ). {\displaystyle A({\boldsymbol {\eta }})} p A casual reader may wish to restrict attention to the first and simplest definition, which corresponds to a single-parameter family of discrete or continuous probability distributions. θ th sufficient statistic should be Section 6.2 outlines some preparatory infrastructure for the rest of the chapter including mixed model-based penalised splines, semiparametric regression, our choice of prior, (mean field) VB and some tricks when standard VB cannot be easily applied, and we describe how we make comparisons with a gold standard (MCMC). φ is called dispersion parameter. p ( η log However, when rewritten into the factorized form, it can be seen that it cannot be expressed in the required form. for another value, and with k Let κϕϕ=E(∂2ℓ(ϕ)/∂ϕ2), κϕϕϕ=E(∂3ℓ(ϕ)/∂ϕ3), κϕϕϕϕ=E(∂4ℓ(ϕ)/∂ϕ4), κϕϕ(ϕ)=∂κϕϕ/∂ϕ, κϕϕϕ(ϕ)=∂κϕϕϕ/∂ϕ, and κϕϕ(ϕϕ)=∂2κϕϕ/∂ϕ2. there are Further, the Bregman divergence in terms of the natural parameters and the log-normalizer equals the Bregman divergence of the dual parameters (expectation parameters), in the opposite order, for the convex conjugate function. However, when the complications above arise standard application of VB methodology is not straightforward to apply. f η Variant 3 shows how to make the parameters identifiable in a convenient way by setting, This page was last edited on 5 January 2021, at 01:51. ∑ As another example, if we take a normal distribution in which the mean and the variance ⁡ This is the case of the Wishart distribution, which is defined over matrices. A factor consisting of a sum where both types of variables are involved (e.g. {\displaystyle \eta ,\eta '} are integrals with respect to the reference measure of the exponential family generated by H . {\displaystyle \textstyle \sum _{i=1}^{k}e^{\eta _{i}}=C}, [ ) 0. ) Exponential families arise naturally as the answer to the following question: what is the maximum-entropy distribution consistent with given constraints on expected values? 1 This requires us to specify a prior distribution p(θ), from which we can obtain the posterior distribution p(θ|x) via Bayes theorem: p(θ|x) = p(x|θ)p(θ) p(x), (9.1) where p(x|θ) is the likelihood. This is in the exponential family form, with: η = µ/σ2 −1/2σ2 (8.21) T(x) = x x2 (8.22) A(η) = µ2 2σ2 +logσ= − η2 1 4η2 − 1 2 log(−2η2) (8.23) h(x) = 1 √ 2π. a factor of the form and η k However, by using the constraint on the natural parameters, the formula for the normal parameters in terms of the natural parameters can be written in a way that is independent on the constant that is added. In the definitions above, the functions T(x), η(θ), and A(η) were apparently arbitrarily defined. C T Two-parameter exponential distribution is the simplest lifetime distributions that is useable in survival analysis and reliability theory. + The frequencies of AB, AcB, ABc, and AcBc in n trials are given in Table 6.1, known as a 2 × 2 contingency table: Table 6.1. Show that if (X2 − X1) and X1 are independent, then the population is either exponential or geometric. 2 {\bigl [}-c\cdot T(x)\,{\bigr ]}} log ⁡ The definition in terms of one real-number parameter can be extended to one real-vector parameter, A family of distributions is said to belong to a vector exponential family if the probability density function (or probability mass function, for discrete distributions) can be written as. ( 1 . 1 Exponential families include many of the most common distributions. η k ( , regardless of the form of the transformation that generates = − (typically Lebesgue measure), one can write Bhattacharya, Prabir Burman, in Theory and Methods of Statistics, 2016. | [9] Many of the standard results for exponential families do not apply to curved exponential families. The vector-parameter form over a single scalar-valued random variable can be trivially expanded to cover a joint distribution over a vector of random variables. ∣ The relative entropy (Kullback–Leibler divergence, KL divergence) of two distributions in an exponential family has a simple expression as the Bregman divergence between the natural parameters with respect to the log-normalizer. John T. Ormerod, in Flexible Bayesian Regression Modelling, 2020. p i χ {\displaystyle \,{\rm {d\,}}H(x)=h(x)\,{\rm {d\,}}x\,} 2 [6] This can be used to exclude a parametric family distribution from being an exponential family. are constrained, such that. for the distribution in this family corresponding a fixed value of the natural parameter . θ Variant 2 demonstrates the fact that the entire set of natural parameters is nonidentifiable: Adding any constant value to the natural parameters has no effect on the resulting distribution. for which ) θ Therefore, the model p y(; ) is not a one-parameter exponential family. , 25(5) (1998) 707–714. (i.e. X A one-parameter exponential family has a monotone non-decreasing likelihood ratio in the sufficient statistic T(x), provided that η(θ) is non-decreasing. log is a normalization constant that is automatically determined by the remaining functions and serves to ensure that the given function is a probability density function (i.e. Normalization is imposed by letting T0 = 1 be one of the constraints. p | Section 6.3 applies the methods described in Section 6.2 to a standard semiparametric regression model (a generalised additive model) which provides a basis for the rest of the chapter. η {\displaystyle {\boldsymbol {\eta }}} + Hence an exponential family in its "natural form" (parametrized by its natural parameter) looks like. 1 X two-parameter exponential family when either of the two parameters is of interest. θ The beta prime distribution is a two-parameter exponential family in the shape parameters $$a \in (0, \infty)$$, $$b \in (0, \infty)$$. In particular, using the properties of the cumulant generating function. (8.24) Note in particular that the univariate Gaussian distribution is a two-parameter distribution and that its suﬃcient statistic is a vector. g A single-parameter exponential family is a set of probability distributions whose probability density function (or probability mass function, for the case of a discrete distribution) can be expressed in the form. The final example is one where integration would be extremely difficult. Examples of nonstandard situations include, but are not limited to: In this chapter we give a tutorial style introduction to VB to fit nonstandard flexible regression methods in the above cases. {\displaystyle f_{X}\!\left(x\mid \theta \right)} There are two important spaces connected with every multivariate exponential family, the natural parameter space and the expectation parameter space. We use cumulative distribution functions (CDF) in order to encompass both discrete and continuous distributions. ( 1 Section 6.4 to Section 6.7 describe our approaches for handing outliers, heteroscedastic noise, overdispersed count data and missing data, respectively. Here, primes denote derivatives with respect to ϕ. ). B {\displaystyle {\boldsymbol {\theta }}\,} + {\displaystyle {\boldsymbol {\chi }}} {\displaystyle \left(\eta _{2}+{\frac {p+1}{2}}\right)(p\log 2+\log |\mathbf {V} |)} ) {\displaystyle f(x)} (which is derived from the one-parameter exponential family assumption). Frequency Distribution in n Trials, Based on this data, we want to test: H0: A and B are independent or negatively dependent (ie, pAB ≤ pApB) vs H1: A and B are positively dependent (ie, pAB > pApB). Any member of that exponential family has cumulative distribution function. {\displaystyle (\mathbf {X} ,\log |\mathbf {X} |).}. T assumes, though this is seldom pointed out, that dH is chosen to be the counting measure on I. Some distributions are exponential families only if some of their parameters are held fixed. ) ) i f Despite this shortcoming VB has shown to be an effective approach to several practical problems, including document retrieval [19], functional magnetic resonance imaging [11,23] and cluster analysis for gene expression data [33]. ′ and the log-partition function is. 2 Similarly, if one is estimating the parameter of a Poisson distribution the use of a gamma prior will lead to another gamma posterior. i Thus, there are only x x ( 1 Comment .        0 x In addition, as above, both of these functions can always be written as functions of {\displaystyle \,{\rm {d\,}}x\,} 1 {\displaystyle {\boldsymbol {\eta }}} {\displaystyle A(x)\ } 1 m log ( We have A1 = 0, A2 = 18, A3 = 20, E(ST)=1, VAR(ST)=2+6/n, μ3(ST) = 8 + 112/n, and, P.K. The exponential family: Conjugate priors Within the Bayesian framework the parameter θ is treated as a random quantity. As mentioned above, as a general rule, the support of an exponential family must remain the same across all parameter settings in the family. This example illustrates a case where using this method is very simple, but the direct calculation would be nearly impossible. A conjugate prior π for the parameter The canonical form is non-unique, since η(θ) can be multiplied by any nonzero constant, provided that T(x) is multiplied by that constant's reciprocal, or a constant c can be added to η(θ) and h(x) multiplied by {\displaystyle \theta '} Jeffreys prior for the Binomial is Beta (1/2,1/2). The problems of finding unbiased level α tests for testing H0: μ ≤ aλ vs H1: μ > aλ and for testing H0: a1λ ≤ μ ≤ a2λ vs H1:μ∉a1λ,a2λr for given a or given a1 < a2 are treated analogously using the methods developed for Problems 1 and 3 of Section 6.9. Other parameters present in the distribution that are not of any interest, or that are already calculated in advance, are called nuisance parameters. x p The normal is a 2 parameter exponential family with natural parameter space (θ 1, θ 2): θ 1 ∈ R, θ 2 < 0. ⁡ A 1 1 The exponential family of distribution is the set of … Also, Truncated extreme value (ϕ > 0, x > 0). ) Semiparametric regression consists of a class of models which includes generalised additive models, generalised additive mixed models, varying coefficient models, geoadditive models and subject-specific curve models, among others (for a relatively comprehensive summary see [29]). M. A. Beg, On the estimation of pr {Y < X} for the two-parameter exponential distribution, Metrika 27(1) (1980) 29–34. η η α ( (x)} − If σ = 1 this is in canonical form, as then η(μ) = μ.