Fisher–Tippett–Gnedenko theorem

The Fisher–Tippett–Gnedenko theorem is a fundamental result in extreme value theory that characterizes the possible limiting distributions for the normalized maxima (or minima) of a sequence of independent and identically distributed random variables as the sample size grows large.^[1] Specifically, if there exist sequences of normalizing constants a_n > 0 and b_n \in \mathbb{R} such that the distribution of (M_n - b_n)/a_n—where M_n is the maximum—converges to a non-degenerate limiting distribution function G, then G must belong to one of three max-stable families: the Gumbel, Fréchet, or reversed Weibull distributions.^[1] This theorem provides a unified framework for understanding the asymptotic behavior of extremes, enabling extrapolation beyond observed data in probabilistic modeling.^[1] The theorem originated from the work of Ronald A. Fisher and Leonard H. C. Tippett, who in 1928 identified the three possible limiting forms through heuristic arguments applied to samples from various parent distributions. Their analysis built on earlier contributions, such as Maurice Fréchet's 1927 exploration of stable distributions for maxima, but Fisher and Tippett provided the initial classification into the three types. Boris V. Gnedenko supplied the first rigorous mathematical proof in 1943, establishing the theorem's validity under general conditions and confirming that no other non-degenerate limits are possible.^[2] This culmination marked a cornerstone of modern extreme value theory, influencing subsequent developments like the generalized extreme value distribution that encompasses all three types via a shape parameter.^[1] The three limiting distributions correspond to different tail behaviors of the underlying random variables: the Gumbel distribution (\gamma = 0) applies to distributions with exponentially decaying tails, such as the normal or exponential, and has the form G(x) = \exp(-\exp(-x)) for x \in \mathbb{R}; the Fréchet distribution (\gamma > 0) suits heavy-tailed cases like the Pareto, given by G(x) = \exp(-x^{-1/\gamma}) for x > 0; and the reversed Weibull distribution (\gamma < 0) fits bounded upper tails, such as the uniform, with G(x) = \exp(-(-x)^{-1/\gamma}) for x < 0.^[1] These forms are unified in the generalized extreme value (GEV) distribution, G(x) = \exp\left(-(1 + \gamma x)^{-1/\gamma}\right), which facilitates parameter estimation and hypothesis testing in applications.^[1] The theorem's implications extend to diverse fields, including hydrology for flood prediction, finance for risk assessment, and environmental science for modeling rare events, where accurate tail estimation is crucial.^[1]

Background and History

Extreme Value Theory Basics

Extreme value theory (EVT) is a subfield of probability and statistics dedicated to the asymptotic analysis of extreme observations, specifically the maxima or minima arising from sequences of independent and identically distributed (i.i.d.) random variables. Unlike classical statistical methods that focus on central tendencies or typical behaviors, EVT addresses the tail regions of distributions, providing tools to model and predict rare, high-impact events such as floods, stock market crashes, or material failures.^[3] This framework draws an analogy to the central limit theorem (CLT), which characterizes the distribution of sample means (or sums) from i.i.d. variables as approximating a normal distribution for large sample sizes, regardless of the underlying distribution (under mild conditions). In contrast, EVT examines the distributional limits of sample extremes, revealing how the largest or smallest values behave as the sample size grows, often converging to one of a small set of universal forms.^[3] Consider a sequence of i.i.d. random variables X_1, \dots, X_n with common cumulative distribution function (cdf) F. Define the sample maximum as M_n = \max\{X_1, \dots, X_n\}. The core question in EVT is whether there exist normalizing constants a_n > 0 and b_n (depending on n and F) such that the normalized maximum (M_n - b_n)/a_n converges in distribution to a non-degenerate limiting cdf G, i.e.,

\lim_{n \to \infty} P\left( \frac{M_n - b_n}{a_n} \le x \right) = G(x)

for all continuity points x of G. Similar setups apply to sample minima by considering -X_i. The existence of such limits hinges on the tail properties of F.^[4] Key prerequisite concepts involve the tail behavior of the parent distribution F. Distributions are classified as heavy-tailed if the right tail decays slowly, typically like a power law (e.g., Pareto or Student's t-distributions, where extreme values are more probable), or light-tailed if the tail decays rapidly, such as exponentially (e.g., normal or exponential distributions, where extremes are rarer). These characteristics determine whether and how normalization leads to a stable limit, influencing applications in risk assessment where heavy tails amplify the likelihood of outliers.^[3] The foundations of EVT trace back to early probabilistic inquiries into extremes, such as Nicholas Bernoulli's 1709 work on the distribution of the largest of n i.i.d. variates, evolving into rigorous asymptotic theory by the early 20th century.^[5] The possible non-degenerate limiting distributions for normalized maxima fall into three types known as Fréchet, Gumbel, and Weibull.^[6]

Origins and Key Contributors

The origins of the Fisher–Tippett–Gnedenko theorem lie in early 20th-century investigations into the asymptotic behavior of extreme values within samples of independent random variables, building on foundational probability theory to address limits distinct from those of central limit theorems. In 1927, Maurice Fréchet advanced the field by deriving possible limiting distributions for sample maxima, identifying two of the three distinct types that form the basis of modern extreme value distributions.^[7] Ronald A. Fisher contributed during the 1920s through his statistical analyses of extremes in biological and agricultural data, such as thread strengths relevant to experimental design, which informed inverse distribution approaches for tail behaviors.^[6] A pivotal milestone occurred in 1928 with the collaborative note by Fisher and Leonard H. C. Tippett, titled "Limiting forms of the frequency distribution of the largest or smallest member of a sample," published in the Proceedings of the Cambridge Philosophical Society. This work systematically classified the three extremal types—later known as Gumbel, Fréchet, and Weibull—and proposed the initial version of the theorem. Richard von Mises (1936) provided sufficient conditions for the weak convergence of the largest order statistic to each of the three types.^[6]^[8]^[9] The theorem's comprehensive formulation emerged in 1943 through Boris V. Gnedenko's paper "Sur la distribution limite du terme maximum d’une série aléatoire," published in the Annals of Mathematics. Gnedenko proved the exhaustive classification of possible non-degenerate limits and specified convergence conditions, unifying and rigorously establishing the prior discoveries.^[6]^[9] Initially termed the Fisher–Tippett theorem, the result was later renamed the Fisher–Tippett–Gnedenko theorem to fully credit all contributors; subsequent extensions, such as Laurens de Haan's developments in the 1970s on multivariate and regular variation aspects, built upon this foundation without altering its core.^[9]

Theorem Statement

For Sample Maxima

The Fisher–Tippett–Gnedenko theorem provides the characterization of the limiting distribution for the normalized sample maximum of independent and identically distributed (i.i.d.) random variables. Let X_1, X_2, \dots, X_n be i.i.d. random variables with common right-continuous cumulative distribution function (cdf) F. Define the sample maximum as M_n = \max\{X_1, \dots, X_n\}. The theorem asserts that if there exist sequences of normalizing constants a_n > 0 and b_n \in \mathbb{R} such that

P\left( \frac{M_n - b_n}{a_n} \leq x \right) \to G(x)

as n \to \infty for all continuity points x of G, where G is a non-degenerate cdf (meaning G does not converge to 0 or 1 for all x \in \mathbb{R}), then G must belong to one of three families: the Fréchet family (with shape parameter \alpha > 0), the Gumbel family, or the Weibull family (with shape parameter \alpha > 0). These three families are unified in modern formulations by the generalized extreme value (GEV) distribution, which serves as the limiting form for maxima and encapsulates all possible non-degenerate limits under appropriate normalization. The cdf of the GEV distribution is given by

G(x) = \exp\left\{ -\left(1 + \xi x\right)^{-1/\xi} \right\}

for \xi \neq 0, defined on the support where $1 + \xi x > 0, and

G(x) = \exp\left( -e^{-x} \right)

for \xi = 0, with support on all real numbers. Here, \xi is the shape parameter: \xi > 0 corresponds to the Fréchet case, \xi = 0 to the Gumbel case, and \xi < 0 to the Weibull case (where \alpha = -1/\xi > 0). This parameterization, often presented in standardized form with location \mu = 0 and scale \sigma = 1, highlights the theorem's conclusion that only these forms arise as limits for sample maxima.

For Sample Minima

The Fisher–Tippett–Gnedenko theorem extends naturally to the sample minima, characterizing the asymptotic distribution of the smallest order statistic from a sequence of independent and identically distributed random variables with cumulative distribution function F. Let m_n = \min\{X_1, \dots, X_n\}. Under suitable conditions, there exist normalizing constants c_n > 0 and d_n \in \mathbb{R} such that

P\left( \frac{m_n - d_n}{c_n} \leq x \right) \to H(x)

as n \to \infty, where H is a non-degenerate limiting distribution function belonging to one of the three extreme value types: Fréchet, Gumbel, or Weibull. This convergence captures the left-tail behavior of F, focusing on extreme low values rather than high ones.^[10] The limiting form H(x) for minima relates directly to the limiting distribution G(x) for sample maxima via the transformation H(x) = 1 - G(-x), which reflects the distributions across the origin to account for the symmetry between minima and maxima. This equivalence arises because the minimum of the X_i corresponds to the negative of the maximum of the transformed variables Y_i = -X_i, which are i.i.d. with distribution function $1 - F(-y). Consequently, the three types of limiting distributions for minima are the same as for maxima but mirrored, ensuring the theorem's applicability to both tails without introducing new forms. The non-degeneracy of the limit requires that the normalizing sequences prevent collapse to a point mass, analogous to the maxima case, and holds provided F belongs to the appropriate domain of attraction for the lower tail. Convergence for sample minima is equivalently framed in terms of the lower tail of F, mirroring how maxima convergence depends on the survival function $1 - F for the upper tail; specifically, the condition for minima of X_i aligns with the upper tail condition for the -X_i. While the theorem is frequently derived from the maxima formulation due to this symmetry, explicitly stating the minima case enhances completeness in extreme value theory applications, such as modeling downside risks in finance or environmental extremes.^[10]

Convergence Conditions

Domains of Attraction

In extreme value theory, a cumulative distribution function F belongs to the domain of attraction of a non-degenerate limiting distribution G if there exist normalizing sequences a_n > 0 and b_n such that the distribution of the normalized sample maximum (M_n - b_n)/a_n converges to G as n \to \infty, where M_n = \max(X_1, \dots, X_n) for i.i.d. random variables X_i with distribution F.^[11]^[6] The domain of attraction for the Fréchet type consists of heavy-tailed distributions where the survival function $1 - F(x) exhibits regular variation at infinity with index -\alpha for some \alpha > 0, meaning \lim_{x \to \infty} \frac{1 - F(tx)}{1 - F(x)} = t^{-\alpha} for all t > 0.^[11]^[12] This characterization relies on Karamata's theory of regular variation, which describes the asymptotic behavior of slowly varying functions L(x) such that $1 - F(x) \sim x^{-\alpha} L(x) as x \to \infty.^[13] Distributions in the domain of attraction of the Gumbel type have lighter tails, typically exponential or subexponential decay, such as the exponential distribution itself or those with tails like the normal and lognormal.^[6]^[1] The domain of attraction for the Weibull type includes distributions with a finite upper endpoint \omega(F) < \infty, such as the uniform or beta distributions on a bounded interval.^[11]^[1] A general sufficient condition for membership in any of these domains is the von Mises condition, which involves the hazard rate or auxiliary function \phi(x) = \frac{1 - F(x)}{f(x)} (where f is the density of F) satisfying \lim_{x \to \omega(F)^-} \phi'(x) = \xi, with \xi > 0 for Fréchet, \xi = 0 for Gumbel, and \xi < 0 for Weibull.^[11]

Choice of Normalizing Sequences

In extreme value theory, the choice of normalizing sequences a_n > 0 and b_n \in \mathbb{R} is crucial for the convergence of the properly normalized sample maximum M_n = \max\{X_1, \dots, X_n\} to one of the extreme value distributions, assuming the parent distribution F belongs to a specific domain of attraction. A standard general method sets the centering constant as b_n = F^{-1}(1 - 1/n), the (1 - 1/n)-quantile of F, which captures the typical scale of the maximum.^[14] The scaling constant a_n can then be determined as a_n = b_{n e} - b_n, where e is the base of the natural logarithm, providing an approximation based on the growth rate of the quantiles; alternatively, a_n may be derived using auxiliary functions specific to the tail behavior of F. For distributions in the Fréchet domain of attraction, characterized by heavy tails with tail index \alpha > 0, the scaling is often a_n \sim n^{1/\alpha} L(n) for some slowly varying function L, while the centering b_n = 0 suffices for standard Pareto-like tails.^[14] In the Gumbel domain of attraction, which includes distributions with lighter tails like the exponential, the scaling approximates a_n \approx 1 / f(b_n), where f is the hazard rate function; for the standard exponential distribution F(x) = 1 - e^{-x} (x > 0), explicit choices are a_n = 1 and b_n = \log n. For the Weibull domain of attraction, relevant when the distribution has a finite upper endpoint \omega < \infty, the centering b_n approaches \omega from below, and the scaling is a_n = \omega - b_n, reflecting the bounded support.^[14] These normalizing sequences are asymptotically equivalent if they satisfy \lim_{n \to \infty} P((M_n - b_n)/a_n \leq x) = G(x) for all x in the support of the limiting extreme value distribution G, ensuring the choice aligns with the domain of attraction.

Extreme Value Distributions

Fréchet Type

The Fréchet distribution serves as the limiting distribution for the normalized maxima of sequences of independent and identically distributed random variables drawn from heavy-tailed parent distributions, as established in the Fisher–Tippett–Gnedenko theorem. It corresponds to the case of positive tail index ξ > 0 within the generalized extreme value (GEV) distribution, unifying the three extreme value types under a single parametric family. The cumulative distribution function (CDF) of the standard Fréchet distribution, parameterized by shape α > 0, is

G_\alpha(x) = \begin{cases} \exp\left( -x^{-\alpha} \right) & x > 0, \\ 0 & x \leq 0. \end{cases}

This form highlights its support on the positive reals and the role of α in controlling tail heaviness, with the tail index ξ = 1/α linking directly to the GEV parameterization. The Fréchet distribution features a heavy right tail, where the survival function satisfies P(X > x) ∼ x^{-α} as x → ∞, reflecting power-law decay characteristic of distributions in its domain of attraction. Moments exist only up to order k < α; the r-th raw moment is E[X^r] = Γ(1 - r/α) for 0 < r < α, where Γ denotes the gamma function. The probability density function (PDF), obtained by differentiating the CDF, is

g_\alpha(x) = \alpha x^{-\alpha-1} \exp\left( -x^{-\alpha} \right), \quad x > 0.

For α > 1, the mean is μ = Γ(1 - 1/α); for α > 2, the variance is σ² = Γ(1 - 2/α) - [Γ(1 - 1/α)]². These expressions underscore the increasing stability of moments as α grows, though the distribution remains positively skewed with infinite higher moments. A canonical example of convergence to the Fréchet limit arises from the Pareto distribution with tail index α > 0 and minimum value 1, whose normalized sample maxima M_n = \max(X_1, \dots, X_n) satisfy (M_n / n^{1/α}) \xrightarrow{d} G_\alpha as n → ∞, using centering constant b_n = 0. This illustrates the theorem's application to power-law tails prevalent in fields like finance and hydrology.

Gumbel Type

The Gumbel distribution, corresponding to the Type I case in the Fisher–Tippett–Gnedenko theorem, arises as the limiting distribution for normalized sample maxima from distributions with light or medium tails that decay exponentially, such as the exponential, normal, gamma, and lognormal distributions.^[1] This places it as the ξ = 0 case within the generalized extreme value distribution framework, distinguishing it from power-law heavy tails or bounded supports.^[15] The cumulative distribution function (CDF) of the standard Gumbel distribution is

G(x) = \exp\left( -e^{-x} \right), \quad x \in \mathbb{R}.

^[16] In its general location-scale form, the CDF is

G(x; \mu, \sigma) = \exp\left( - \exp\left( -\frac{x - \mu}{\sigma} \right) \right),

where μ ∈ ℝ is the location parameter and σ > 0 is the scale parameter.^[16] The probability density function (PDF) is then

g(x; \mu, \sigma) = \frac{1}{\sigma} \exp\left( -\frac{x - \mu}{\sigma} - \exp\left( -\frac{x - \mu}{\sigma} \right) \right).

^[16] The Gumbel distribution is asymmetric and right-skewed, with a skewness coefficient of approximately 1.14.^[16] Key properties include a mean of μ + γ σ, where γ ≈ 0.57721 is the Euler–Mascheroni constant, and a variance of \frac{\pi^2}{6} \sigma^2 ≈ 1.64493 \sigma^2.^[16] The mode occurs at the location parameter μ.^[16] These moments highlight its utility in modeling maxima where the tail behavior leads to a shifted and scaled exponential limit. A representative example of convergence is the standard exponential distribution with CDF F(x) = 1 - e^{-x} for x ≥ 0; the normalized sample maxima M_n satisfy P(M_n - \log n \leq x) \to G(x) as n \to \infty, using normalizing constants a_n = 1 and b_n = \log n.^[15] Similarly, maxima from normal distributions converge to the Gumbel after appropriate normalization involving the inverse CDF at 1 - 1/n and auxiliary functions.^[1] The Gumbel form is widely applied in fields like reliability engineering and meteorology for predicting extreme events from such underlying distributions.^[6]

Weibull Type

The Weibull type distribution serves as the limiting form in the Fisher–Tippett–Gnedenko theorem for the normalized sample maxima of distributions possessing a finite upper endpoint, capturing short-tailed behaviors near that boundary.^[8] This case arises when the underlying distribution F has an upper endpoint \omega < \infty, and appropriate normalizing constants a_n > 0 and b_n (with b_n \to \omega) ensure convergence of [F(a_n x + b_n)]^n to the Weibull cumulative distribution function (CDF). In this context, the theorem identifies the Weibull as one of three max-stable distributions, alongside the Fréchet and Gumbel types.^[1] The standardized CDF of the Weibull type is

G_{\alpha}(x) = \begin{cases} \exp\left( -(-x)^{\alpha} \right) & x < 0, \\ 1 & x \geq 0, \end{cases}

where \alpha > 0 governs the shape.^[11] This distribution has support on (-\infty, 0], reflecting a reflection from the finite upper endpoint after normalization.^[8] Within the generalized extreme value (GEV) family, it corresponds to the shape parameter \xi = -1/\alpha < 0, distinguishing it from heavy-tailed (\xi > 0) and light-tailed unbounded (\xi = 0) cases.^[1] All moments are finite due to the bounded support, enabling reliable estimation in applications with constrained extremes.^[11] For maxima, the distribution exhibits left-skewness, while the corresponding form for minima is right-skewed.^[8] The probability density function (PDF) is

g_{\alpha}(x) = \alpha (-x)^{\alpha - 1} \exp\left( -(-x)^{\alpha} \right), \quad x < 0.

This PDF connects directly to the standard Weibull distribution, which models minima with CDF $1 - \exp(-x^{\alpha}) for x > 0, via a reflection and rescaling transformation that reverses the support for upper-bounded maxima.^[8] The reversed Weibull form specifically applies to distributions F with finite upper endpoint \omega, where the normalized exceedances above the lower tail (or distances to \omega) follow this structure after adjustment.^[1] A canonical example of convergence is the uniform distribution on [0, 1], which has upper endpoint \omega = 1; the normalized n(M_n - 1) converges in distribution to the Weibull with \alpha = 1, yielding CDF \exp(x) for x < 0.^[8] This illustrates polynomial-like behavior near the endpoint, with the limit capturing the rapid approach to \omega.^[11] The Weibull type, like the Fréchet and Gumbel types, is max-stable, satisfying the max-stability condition G(x)^n = G(c_n x + d_n) for suitable c_n > 0 and d_n. This underpins its role in modeling maxima of upper-bounded processes and extends to multivariate extremes via appropriate copulas.^[1]

Proof Outline

Reduction to Exponential Case

The proof of the Fisher–Tippett–Gnedenko theorem proceeds by demonstrating that any distribution function F belonging to the domain of attraction of a non-degenerate extreme value distribution G can be transformed into an exponential distribution, whose sample maxima are known to converge to the Gumbel distribution after appropriate normalization; the inverse transformation then yields the original limit G. This reduction leverages the fact that the exponential distribution has a simple tail behavior that facilitates analysis of extremes, allowing the theorem to characterize the possible limiting forms as Fréchet, Gumbel, or Weibull types. Gnedenko's original proof (1943) established this classification by showing that the possible non-degenerate limits correspond to three tail equivalence classes: exponential (leading to Gumbel), power-law (Fréchet), and bounded upper endpoint (Weibull), using arguments based on characteristic functions, tail probabilities, and tightness of distributions to rule out other forms.^[2] A central step involves identifying a strictly increasing, continuous function h such that the transformed variables h(X_i) follow an approximate exponential distribution, i.e., $1 - F(h^{-1}(y)) \sim e^{-y} for large y. Under this transformation, the maxima satisfy M_n^h \approx h(M_n), where M_n^h = \max\{h(X_1), \dots, h(X_n)\}; since the maxima of exponentials converge to a Gumbel limit after centering and scaling, the original M_n inherits this convergence via the continuity and monotonicity of h. This approach embeds the domains of attraction of the Fréchet and Weibull distributions into that of the exponential (Gumbel) case, unifying the proof strategy.^[17] For distributions in the Fréchet domain (heavy-tailed with \alpha > 0), the transformation employs a power function, such as h(x) = x^{-1/\alpha} (possibly modulated by a slowly varying function), which maps the regularly varying tail to an exponential form. In contrast, for the Weibull domain (distributions with finite right endpoint), a linear transformation near the endpoint, like h(x) = -(-x)^{1/\alpha} for x approaching the endpoint from below, achieves the reduction by linearizing the behavior close to the boundary. These specific transformations ensure the tail equivalence required for the exponential approximation.^[17] Subsequent developments, building on Gnedenko's proof, such as the work of de Haan (1970), further characterize the domains of attraction using the tail quantile function U(t) = F^{-1}(1 - 1/t), showing that for F in the domain of attraction of a generalized extreme value distribution, U(nt)/U(n) \to (1 + \xi \log t)^{1/\xi} as n \to \infty, where \xi is the shape parameter (\xi > 0 for Fréchet, \xi < 0 for Weibull, \xi = 0 for Gumbel). This convergence relies on the slow variation of an auxiliary function in the tails, such as the mean excess or a related scaling measure, which captures the asymptotic tail regularity without rapid fluctuations. The slow variation property ensures that the normalizing sequences a_n and b_n can be chosen to align the transformed quantiles with exponential spacing.^[17]

Generalization to Arbitrary Distributions

The classical Fisher–Tippett–Gnedenko theorem applies to independent and identically distributed (i.i.d.) univariate random variables, limiting its direct use for dependent or multidimensional data common in practice.^[18] To address this, extensions have been developed for stationary sequences under weak dependence conditions. In particular, Leadbetter (1974) established conditions D (distributional mixing) and U (uniform tail dependence) that ensure the limiting distribution of maxima remains one of the extreme value types, provided the sequence satisfies these mixing properties to control clustering of extremes.^[18] These conditions relax the i.i.d. requirement while preserving the theorem's core convergence results, enabling applications to time series like financial returns or environmental measurements where observations are serially dependent but asymptotically independent at long lags.^[19] Further generalizations extend the theorem to multivariate settings, where the componentwise maxima of vectors in \mathbb{R}^d converge, after normalization, to multivariate extreme value distributions characterized by their margins and dependence structure.^[20] Pickands (1975) laid foundational work for such limits by developing inference methods for extreme order statistics, which underpin multivariate extensions by incorporating spectral measures to model joint tail dependence.^[20] These multivariate forms capture scenarios like joint flood risks across regions, where extremes in multiple variables occur simultaneously due to shared drivers. A complementary approach, the peaks-over-threshold (POT) method, shifts focus from block maxima to exceedances above high thresholds, showing that such excesses converge to the generalized Pareto distribution under suitable conditions.^[21] This result, established independently by Balkema and de Haan (1974) for residual lifetimes and by Pickands (1975) via a point process characterization of extremes, provides a more data-efficient alternative to the block maxima method by utilizing all observations above the threshold rather than just periodic maxima.^[21]^[20] The POT framework, often combined with point process theory, extends naturally to dependent and multivariate cases, addressing limitations of the i.i.d. assumption through mixing conditions similar to Leadbetter's.^[20] In contemporary risk analysis, block maxima and POT methods are routinely compared for their bias-variance trade-offs, with POT generally favored for higher statistical efficiency in estimating tail risks, though block maxima remains simpler for long-horizon predictions.^[22] Recent applications in the 2020s, including projections of climate becoming unpredictable and chaotic by 2052 using generalized Pareto distributions (Scarpa, 2024) and future ozone episode frequencies in China under diverse emission pathways (Wang et al., 2025), have leveraged these generalizations for modeling climate extremes under non-stationary conditions influenced by global warming, integrating multivariate POT with spatial dependence to improve hazard assessments.^[23]^[24]^[25]^[26] These developments highlight how the theorem's extensions via weak dependence and threshold modeling bridge classical theory to practical, high-dimensional problems in fields like environmental science and finance.^[22]