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Generalized inverse Gaussian distribution

The generalized inverse Gaussian distribution (GIG) is a three-parameter family of continuous probability distributions supported on the positive real line, with probability density function
f(x; p, a, b) = \frac{(a/b)^{p/2}}{2 K_p(\sqrt{ab})} x^{p-1} \exp\left\{ -\frac{1}{2} (a x + b/x) \right\}
for x > 0, where p \in \mathbb{R} is the shape parameter, a > 0 and b > 0 are scale parameters, and K_p(\cdot) denotes the modified Bessel function of the second kind of order p.^[1] This distribution was originally introduced by the French statistician Étienne Halphen in 1941 as part of a system of distributions for frequency analysis of hydrological data, such as river flows. It was rediscovered and popularized in the 1970s by Danish statistician Ole Barndorff-Nielsen, who coined the name "generalized inverse Gaussian distribution" during his work on infinitely divisible distributions and stochastic processes in physics and finance.^[2] A comprehensive treatment of its statistical properties, including moments, cumulants, and inference methods, was provided by Bent Jørgensen in his 1982 monograph, which established the GIG as a fundamental tool in theoretical and applied statistics.^[2] Notable special cases of the GIG include the gamma distribution (limit as b \to 0), the inverse gamma distribution (limit as a \to 0), and the inverse Gaussian distribution (when p = -1/2).^[1] The GIG's flexibility in capturing both heavy-tailed and light-tailed behaviors has led to widespread applications, particularly in Bayesian statistics for constructing conjugate priors and facilitating Markov chain Monte Carlo sampling in hierarchical models, as well as in financial engineering for modeling stochastic volatility and Lévy processes.^[1] Its infinite divisibility further enables its use in simulating compound Poisson processes and other continuous-time models in risk analysis.^[3]

Introduction

Definition

The generalized inverse Gaussian distribution is a three-parameter family of continuous probability distributions supported on the positive real line. A random variable X follows a generalized inverse Gaussian distribution with parameters p \in \mathbb{R}, a > 0, and b > 0, denoted X \sim \mathrm{GIG}(p, a, b), if its probability density function is given by

f(x; p, a, b) = \frac{(a/b)^{p/2}}{2 K_p(\sqrt{a b})} \, x^{p-1} \exp\left( -\frac{a x + b / x}{2} \right), \quad x > 0,

where K_p(\cdot) denotes the modified Bessel function of the second kind of order p.^[2] The parameters a and b control the scale and shape through the exponential term, while p influences the power-law behavior near the origin and at infinity. The support is strictly the positive reals (x > 0), ensuring the distribution is defined for positive-valued random variables, such as reciprocals or transformations in stochastic processes.^[2] The normalization constant \frac{(a/b)^{p/2}}{2 K_p(\sqrt{a b})} arises from the requirement that the density integrates to unity over (0, \infty). Specifically, the integral of the unnormalized density x^{p-1} \exp\left( -\frac{a x + b / x}{2} \right) equals $2 K_p(\sqrt{a b}), a property derived from the integral representation of the modified Bessel function of the second kind. This function K_p(z) ensures integrability for the given parameter constraints, as it converges for z > 0 and all real p, providing the exact normalizing factor.^[2]

Historical background

The generalized inverse Gaussian distribution was first introduced by French statistician and hydrologist Étienne Halphen in 1941 as part of a three-parameter family of distributions developed for analyzing hydrological data, such as river flow frequencies. Halphen's work aimed to model natural phenomena with heavy tails and positive support, and the distribution he proposed—now recognized as the generalized inverse Gaussian—emerged from efforts to generalize earlier special cases like the reciprocal of the gamma distribution. This family, often referred to in hydrology as the Halphen system, included the generalized inverse Gaussian as one of its core types, providing a flexible framework for continuous positive variables.^[4] The distribution saw limited initial adoption outside hydrological contexts but gained renewed attention in the 1970s through the work of Danish statistician Ole Barndorff-Nielsen, who rediscovered and popularized it for broader statistical applications. Barndorff-Nielsen integrated the distribution into models for stochastic processes, particularly in physics and spatial statistics, emphasizing its role in representing infinitely divisible laws.^[5] A pivotal contribution was the 1977 paper by Barndorff-Nielsen and Christian Halgreen, which established the infinite divisibility of the generalized inverse Gaussian and the related hyperbolic distribution, facilitating its use in Lévy processes and compound Poisson models.^[5] This popularization marked a shift from Halphen's specialized hydrological focus to general statistical theory, evolving the distribution from its special cases—such as the inverse Gaussian, originally derived in 1915 for Brownian motion first-passage times and formalized by Maurice Tweedie in 1957—to a versatile three-parameter form applicable across disciplines.^[6] Further consolidation came with Bent Jørgensen's 1982 monograph, which provided a comprehensive treatment of its statistical properties and inferential methods, solidifying its place in probability theory.^[2]

Parametrizations

Standard parametrization

The standard parametrization of the generalized inverse Gaussian (GIG) distribution employs three parameters: a > 0, b > 0, and p \in \mathbb{R}. In this formulation, the probability density function for a random variable X supported on x > 0 is given by

f(x; a, b, p) = \frac{(a/b)^{p/2}}{2 K_p(\sqrt{ab})} \, x^{p-1} \exp\left( -\frac{1}{2} (a x + b/x) \right),

where K_p(\cdot) denotes the modified Bessel function of the second kind of order p.^[1] This parametrization, originally developed in the context of first hitting time models, provides a flexible framework for modeling positive random variables with varying tail behaviors. The parameter a > 0 governs the exponential decay in the density for large values of x, influencing the right tail heaviness through the term a x. Similarly, b > 0 controls the decay for small x via the inverse term b/x, affecting the behavior near zero.^[1] The power parameter p \in \mathbb{R} shapes the polynomial component x^{p-1}, which determines the power-law-like behavior close to the origin (for p < 1) and contributes to the overall flexibility in tail asymmetry. Together, these parameters allow the GIG to encompass a range of distributions, including gamma and inverse gamma as limiting cases when one of a or b approaches zero under appropriate conditions on p. For the distribution to be well-defined with a > 0 and b > 0, the normalizing constant must ensure the density integrates to unity over (0, \infty), which is achieved through the factor involving K_p(\sqrt{a b}). The modified Bessel function K_p(z) exists and is positive for all real p and z > 0, guaranteeing the existence of the GIG density under these positivity constraints on a and b.^[1] These conditions prevent divergences in the integral, as the exponential terms provide sufficient decay while the Bessel function accounts for the precise normalization. In this standard notation, key distributional quantities such as the mean take the form \mathbb{E}[X] = \sqrt{b/a} \, K_{p+1}(\sqrt{a b}) / K_p(\sqrt{a b}), highlighting the interplay between the parameters in determining location-scale properties. This expression, derived from the moment-generating properties, underscores the role of the ratio \sqrt{b/a} in scaling the mean relative to the parameter balance.^[1]

Alternative parametrizations

The generalized inverse Gaussian (GIG) distribution admits several alternative parametrizations that facilitate analysis in specific contexts, such as deriving moments or studying symmetry properties. One common form employs parameters \lambda, \chi, and \psi, where \lambda \in \mathbb{R} is a shape parameter, and \chi, \psi \geq 0 control the scale and asymmetry. The probability density function (PDF) is given by

f(x; \lambda, \chi, \psi) = \frac{ \left( \frac{\psi}{\chi} \right)^{\lambda / 2} }{ 2 K_{\lambda} \left( \sqrt{\psi \chi} \right) } x^{\lambda - 1} \exp\left( -\frac{\chi / x + \psi x}{2} \right), \quad x > 0,

with K_{\lambda}(\cdot) denoting the modified Bessel function of the second kind.^[2] This parametrization is equivalent to the standard form with parameters p, a, b via the substitution \lambda = p, \chi = b, \psi = a, which directly maps the exponential terms and normalizing constant without altering the distributional structure.^[2] Another reparametrization introduces a concentration parameter \theta = \sqrt{\chi \psi} and a scaling asymmetry parameter \eta = \sqrt{\psi / \chi}, transforming the PDF to

f(x; \lambda, \theta, \eta) = \frac{ \eta^{\lambda} }{ 2 K_{\lambda} (\theta) } x^{\lambda - 1} \exp\left( -\frac{\theta}{2} \left( \eta x + \frac{1}{\eta x} \right) \right), \quad x > 0.

This equivalence follows from substituting \chi = \theta / \eta and \psi = \theta \eta into the (\lambda, \chi, \psi) form, which symmetrizes the exponent around the term \eta x + 1/(\eta x) while preserving the Bessel normalizing factor through \sqrt{\chi \psi} = \theta and (\psi / \chi)^{\lambda / 2} = \eta^{\lambda}.^[2] The (\lambda, \chi, \psi) parameters prove convenient in Bayesian analyses, where they simplify expressions for conjugate priors and mixture representations.^[2] In contrast, the (\theta, \eta) form suits physical modeling scenarios, as \theta governs overall concentration and \eta captures directional asymmetry in the distribution tails.^[7] Halgreen's parametrization, tailored for investigations into infinite divisibility, employs a similar structure but emphasizes parameters that highlight self-decomposability properties, often expressed in terms of \nu, \alpha^2, and \beta^2 with the PDF proportional to x^{\nu-1} \exp\left( -\frac{\beta^2 x + \alpha^2 / x}{2} \right).^[8] This maps to the (\lambda, \chi, \psi) form via \nu = \lambda, \chi = \alpha^2, \psi = \beta^2, with the distribution being self-decomposable for \lambda \leq 0.^[9]

Properties

Moments and cumulants

The mean of a random variable X following the generalized inverse Gaussian distribution GIG(p, a, b) with a > 0, b > 0, and p \in \mathbb{R} is given by

\mu = \mathbb{E}[X] = \sqrt{\frac{b}{a}} \frac{K_{p+1}(\sqrt{ab})}{K_p(\sqrt{ab})},

where K_\nu(z) denotes the modified Bessel function of the second kind of order \nu.^[2] The variance is then

\sigma^2 = \mathrm{Var}(X) = \frac{b}{a} \frac{K_{p+2}(\sqrt{ab})}{K_p(\sqrt{ab})} - \mu^2.

This expression simplifies to \sigma^2 = \mu^2 \left( \frac{K_{p+2}(\sqrt{ab}) K_p(\sqrt{ab})}{K_{p+1}^2(\sqrt{ab})} - 1 \right), highlighting its dependence on ratios of consecutive Bessel functions.^[2] Higher-order moments are expressed in closed form as

\mathbb{E}[X^r] = \left( \frac{b}{a} \right)^{r/2} \frac{K_{p+r}(\sqrt{ab})}{K_p(\sqrt{ab})}

for real r such that p + r > 0 to ensure convergence. This general formula facilitates computation of skewness, kurtosis, and other measures by substituting appropriate r. For instance, the third central moment determines skewness via \gamma_1 = \mathbb{E}[(X - \mu)^3] / \sigma^3, derived from the raw moments above.^[2] Cumulants \kappa_s of the GIG distribution satisfy \kappa_1 = \mu (the mean) and \kappa_2 = \sigma^2 (the variance), with higher cumulants obtainable through recursive relations involving derivatives of the logarithm of the normalizing constant, specifically \frac{\partial}{\partial p} \log K_p(\sqrt{ab}) and related terms. These recursions are

\kappa_{s+1} = \frac{b}{a} \frac{K_{p+1}(\sqrt{ab})}{K_p(\sqrt{ab})} \kappa_s + (s-1) \kappa_{s-1} \frac{\partial}{\partial p} \log K_p(\sqrt{ab}),

allowing sequential computation starting from the first two cumulants.^[2] Asymptotic behaviors of moments and cumulants emerge in limiting regimes of the parameters. For large \sqrt{ab}, the distribution approximates a normal distribution with the above mean and variance, where higher cumulants \kappa_s for s \geq 3 become negligible relative to powers of \sigma^2. Conversely, when b \to 0 with fixed a > 0 and p > 0, the GIG converges to a gamma distribution with shape p and rate a/2, yielding moments asymptotic to those of \mathrm{Gamma}(p, a/2); similarly, for a \to 0 with fixed b > 0 and p < 0, it approaches an inverse gamma distribution with shape -p and scale b/2. These limits provide useful approximations for extreme parameter values.^[2]

Mode and median

The mode of the generalized inverse Gaussian (GIG) distribution is obtained by maximizing its probability density function, which involves setting the derivative of the log-density to zero and solving the resulting quadratic equation a m^2 - 2(p-1)m - b = 0, where a > 0, b > 0, and the parameters follow the standard parametrization. The relevant positive root provides the mode

m = \frac{(p-1) + \sqrt{(p-1)^2 + ab}}{a},

valid under conditions ensuring an interior maximum, such as p > 1, where the distribution is unimodal with the mode in (0, \infty).^[2] When |p-1| \ll \sqrt{ab}, the mode approximates \sqrt{b/a}.^[2] The GIG distribution exhibits varying behavior across parameter regimes; for p > 1, the mode shifts toward larger values, reflecting lighter tails, while for negative p, the mode approaches zero, and the distribution develops heavy tails due to the dominance of the x^{p-1} term near the origin.^[2] Unlike the mean (detailed in the moments section), the mode provides a robust location measure less influenced by extreme values in heavy-tailed cases.^[2] The median of the GIG distribution lacks a closed-form expression and must be approximated or computed numerically. Common approaches include the saddlepoint approximation, which leverages the cumulant generating function for accurate tail and central quantile estimates, and the Cornish-Fisher expansion, which adjusts the normal quantile using skewness and kurtosis. Alternatively, numerical methods such as root-finding on the cumulative distribution function (involving Bessel function evaluations) or quadrature-based inversion yield precise medians for specific parameters. These techniques are essential for inference, as the median often lies between the mode and mean in skewed GIG variants.

Generating functions

The characteristic function of a random variable X following the generalized inverse Gaussian distribution GIG(p, a, b) is given by

\phi(t) = \mathbb{E}[e^{i t X}] = \left( \frac{a}{a - 2 i t} \right)^{p/2} \frac{K_p \left( \sqrt{(a - 2 i t) b} \right)}{K_p (\sqrt{a b})}, \tag{1}

where K_p(\cdot) denotes the modified Bessel function of the second kind of order p, and the expression holds for parameters such that the distribution is defined (typically a > 0, b > 0, p \in \mathbb{R}).^[2] This form is derived by direct evaluation of the integral \phi(t) = \int_0^\infty e^{i t x} f(x) \, dx, where f(x) is the probability density function of the GIG, leveraging integral representations of the modified Bessel functions to simplify the resulting expression.^[2] The moment generating function (MGF) follows analogously by replacing i t with t in (1), yielding

M(t) = \mathbb{E}[e^{t X}] = \left( \frac{a}{a - 2 t} \right)^{p/2} \frac{K_p \left( \sqrt{(a - 2 t) b} \right)}{K_p (\sqrt{a b})}, \tag{2}

valid for t < a/2 to ensure convergence.^[2] This MGF serves as a foundational tool for deriving cumulants through logarithmic differentiation and expansion, facilitating analysis of higher-order moments without explicit computation.^[2] Since the GIG is a continuous distribution supported on (0, \infty), the probability generating function is not applicable. However, the Laplace transform, \mathbb{E}[e^{-s X}] for s > 0, takes the form

\mathbb{E}[e^{-s X}] = \left( \frac{a}{a + 2 s} \right)^{p/2} \frac{K_p \left( \sqrt{(a + 2 s) b} \right)}{K_p (\sqrt{a b})}, \tag{3}

obtained similarly via substitution in the characteristic function or direct integration.^[2] These generating functions underpin key probabilistic properties, such as independence criteria in products of distributions. For instance, if X \sim GIG and Y \sim Gamma are independent, then X and Y/X are independent if and only if X follows a specific GIG subclass, with the joint characteristic function factoring accordingly.^[10]

Entropy and infinite divisibility

The differential entropy H of a random variable following the generalized inverse Gaussian (GIG) distribution with parameters p, a > 0, and b > 0 is given by

H = -\frac{1}{2} \left[ p \ln \left( \frac{a}{b} \right) + \frac{a + b}{ \sqrt{a b} } \frac{K_{p+1} (\sqrt{a b}) }{K_p (\sqrt{a b}) } + \ln (2 \pi) + \ln K_p (\sqrt{a b}) + \psi (p) \right],

where K_\nu(\cdot) denotes the modified Bessel function of the second kind of order \nu, and \psi(\cdot) is the digamma function. This expression arises from integrating the negative logarithm of the GIG probability density function and simplifies using properties of the normalizing constant involving the Bessel function. A notable property of this entropy is its maximization under fixed second moment constraints when the distribution achieves symmetry, specifically when a = b, leading to a reciprocal gamma form that balances the shape parameters for peak uncertainty. This maximization aligns with the GIG's role as a maximum-entropy distribution subject to constraints on the mean of the logarithm and reciprocal, highlighting its information-theoretic efficiency in modeling positive data with specified moments. The GIG distribution is infinitely divisible for all parameter values p \in \mathbb{R}, a > 0, b > 0. This property follows from the complete monotonicity of the derivative of its Laplace transform, as established via Grosswald's representation of Bessel functions, allowing decomposition into sums of independent random variables with the same distribution scaled appropriately. Consequently, the GIG serves as the marginal distribution of a Lévy process at unit time, with its cumulant function expressible in Lévy-Khintchine form involving an integral representation that ensures non-negativity and complete monotonicity for positive Lévy measures. Although the class of GIG distributions is not closed under convolution—the sum of independent GIG variables generally does not follow a GIG—the infinite divisibility implies that any GIG can be represented as the sum of n i.i.d. components for arbitrary n > 0, facilitating its use in compound processes and stochastic modeling.

Special cases

The generalized inverse Gaussian (GIG) distribution includes the inverse Gaussian distribution as a special case when p = -\frac{1}{2}. With parameters a = \frac{\lambda}{\mu} and b = \frac{\lambda}{\mu^2}, where \mu > 0 is the mean and \lambda > 0 is the shape parameter of the inverse Gaussian, the GIG density simplifies exactly to the inverse Gaussian form due to the closed-form evaluation of the modified Bessel function of the second kind at order -\frac{1}{2}, given by K_{-\frac{1}{2}}(z) = \sqrt{\frac{\pi}{2z}} e^{-z}. This yields a density proportional to x^{-\frac{3}{2}} \exp\left( -\frac{\lambda (x - \mu)^2}{2 \mu^2 x} \right) for x > 0. The GIG distribution recovers the gamma distribution in the limiting case as b \to 0^+, with p = \alpha > 0 and a = 2\beta, where \alpha > 0 and \beta > 0 are the shape and rate parameters, respectively. In this limit, the influence of the b/x term in the exponent vanishes, and the normalizing constant involving K_p(\sqrt{ab}) approaches the reciprocal of the gamma function via the small-argument asymptotics of the Bessel function, resulting in the standard gamma density f(x) = \frac{\beta^\alpha}{\Gamma(\alpha)} x^{\alpha-1} e^{-\beta x} for x > 0. Analogously, the inverse gamma distribution arises as a \to 0^+, with p = -\alpha < 0 and b = 2\beta, corresponding to shape \alpha > 0 and scale \beta > 0. Here, the a x term in the exponent disappears, and the limiting normalization yields the inverse gamma density f(x) = \frac{\beta^\alpha}{\Gamma(\alpha)} x^{-\alpha-1} e^{-\beta/x} for x > 0. Boundary behaviors of the GIG distribution occur when one or both of a and b approach zero. Specifically, setting one to zero while keeping the other positive leads to transitions to exponential-tailed distributions like the gamma or inverse gamma, as detailed above, with explicit parameter mappings ensuring the densities match. When both a \to 0^+ and b \to 0^+ simultaneously for fixed p, the exponential terms approach unity, reducing the density to a power-law form proportional to x^{p-1} for x > 0, though proper normalization requires -1 < p < 0 for integrability over the positive reals.^[11]

Conjugate priors and mixtures

The generalized inverse Gaussian (GIG) distribution with parameters (\lambda, \chi, \psi) serves as a conjugate prior for the precision parameter \tau of a normal distribution in variance-mean mixture models.^[12] In this setup, the prior on \tau is GIG(\lambda, \chi, \psi), and the joint prior incorporates a normal distribution for the mean conditional on \tau. This conjugacy ensures that the posterior distribution remains in the GIG family after observing data from a normal likelihood.^[12] The GIG distribution also plays a key role in constructing mixture models for enhanced flexibility in modeling heavy tails and overdispersion. A Poisson-GIG compound distribution, where the Poisson rate is mixed with a GIG prior, yields the Sichel distribution, which is particularly useful for capturing overdispersion in count data such as word frequencies.^[13] Similarly, a normal variance-mean mixture with GIG mixing on the variance produces the generalized hyperbolic distribution; a special case arises when \chi = 0, resulting in the variance-gamma distribution, widely applied in financial modeling for asset returns with skewness and kurtosis.^[14] The infinite divisibility of the GIG distribution underpins its suitability for these stable mixtures, as it allows the formation of infinitely divisible compound processes without introducing discontinuities.^[5]

Applications

Bayesian statistics

The generalized inverse Gaussian (GIG) distribution plays a key role in Bayesian inference as a conjugate prior for the precision parameter (inverse variance) of a normal likelihood, particularly when the mean is either known or assigned a separate normal prior. This conjugacy results in a posterior distribution that is also GIG, with hyperparameters updated based on the sufficient statistics from the data, such as the sample sum of squares and size. This property enables exact analytical expressions for posterior moments and predictive distributions without requiring numerical approximation, facilitating straightforward inference in univariate and multivariate normal models.^[12]^[15] In hierarchical Bayesian models, the GIG is frequently adopted for specifying variance components, offering flexibility in generalized linear mixed models (GLMMs) and spatial modeling frameworks. For example, in geostatistical applications like kriging, GIG priors on error variances or nugget effects accommodate non-Gaussian spatial processes, allowing for robust prediction under heteroscedasticity or heavy-tailed innovations. This approach enhances model fit in datasets with clustered or spatially correlated observations, such as environmental monitoring, by capturing complex dependence structures through layered variance specifications.^[16]^[17] Computationally, Bayesian analyses involving GIG priors benefit from direct posterior updates in Gibbs samplers due to conjugacy, which simplifies MCMC implementation compared to non-conjugate setups. When full conditionals are not standard, the Griddy-Gibbs method approximates sampling from GIG-related posteriors by gridding the parameter space and evaluating densities at discrete points, ensuring efficient exploration of the posterior. A key advantage over the more rigid inverse gamma prior is the GIG's tunable tail behavior—ranging from sub-exponential light tails to power-law heavy tails via its shape parameter—enabling better accommodation of outliers or multimodal posteriors in robust inference.^[18]^[19] Recent advancements in MCMC for GIG-involved models include novel rejection sampling generators that decompose the GIG density for faster variate production, as developed by Zhang and Reiter (2022), improving scalability in high-dimensional hierarchical settings like large-scale spatial Bayesian analyses. These methods reduce computational bottlenecks in post-2020 applications, such as integrated nested Laplace approximations or variational Bayes for GIG-based priors.^[20]

Other fields

In finance, the generalized inverse Gaussian (GIG) distribution serves as the mixing distribution in generalized hyperbolic (GH) Lévy processes, enabling the modeling of asset prices with heavy tails, skewness, and semi-heavy tails to better capture empirical return distributions compared to normal models.^[21] These processes, particularly the normal inverse Gaussian subclass, have been applied to option pricing and risk management, providing superior fits to high-frequency financial data. A 2023 review highlights the growing adoption of GH variants for financial returns due to their flexibility in replicating stylized facts like volatility clustering.^[22] In linguistics, the Sichel distribution—a Poisson-GIG mixture—models word frequency counts in large corpora, accommodating overdispersion and power-law tails observed in natural language texts.^[23] This approach yields excellent fits for very long texts, outperforming simpler models like the negative binomial, as demonstrated in analyses of sentence lengths and vocabulary distributions. The model, originally proposed by Sichel in the 1980s, remains relevant for computational text analysis tasks such as topic modeling and information retrieval. Ole Barndorff-Nielsen originally developed the GIG distribution in the context of physical processes, inspired by studies of wind-blown sand dynamics where it parameterized hyperbolic distributions for particle displacement and turbulence modeling.^[24] Extending this, GIG-based stochastic volatility models, such as those using normal inverse Gaussian subordinators, simulate irregular jumps and persistence in physical systems like fluid flows and geophysical phenomena. In geostatistics, GIG mixing enhances spatial correlation models by introducing flexible variance structures for non-Gaussian random fields in environmental monitoring.^[25] Recent applications in the 2020s include GIG-based priors in machine learning for robust sparse regression, where expectation propagation with GIG mixtures induces sparsity while handling heavy-tailed noise in high-dimensional data.^[26] In epidemiology, GIG mixtures appear in frailty models for survival analysis of disease progression, such as promotion time cure models for chronic conditions, improving estimates of heterogeneous risks in population studies.^[27]

References

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