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For $\alpha >0$, the standard Fréchet distribution has distribution function ${\Phi }_{\alpha }(x)=e^{-x^{-\alpha }}$ for $x>0$ and zero for $x\le 0$. Its maximum domain of attraction consists of distribution functions with an infinite right endpoint of a distribution and a survival function with regular variation of index $-\alpha$.

For a nondegenerate distribution function $G$, membership $F\in D(G)$ in its maximum domain of attraction means that there exist $a_n>0,b_n\in \mathbb{R}$ such thatat every continuity point of $G$. A max-stable distribution satisfies: for each positive integer $k$, there exist $c_k>0,d_k\in \mathbb{R}$ such that $G(c_{k}x+d_k{)}^k=G(x)$. Thus a maximum of $k$ independent copies has the same probability distribution after a change of location and scale.

Suppose first that $F\in D(G)$. For fixed $k$, the distribution functions $H_n=F^{kn}$ have the two nondegenerate limitsThe given convergence-of-types result supplies constants $a>0,b$ such that $G(x{)}^k=G(ax+b)$. Equality extends from continuity points to all points by right continuity. Substitute $x=(z-b)/a$ to obtain $G((z-b)/a{)}^k=G(z)$, which proves max-stability with $c_k=1/a,d_k=-b/a$. Conversely, if $G$ is max-stable, take $F=G$ and its max-stability constants for $k=n$. Then $F(c_{n}x+d_n{)}^n=G(x)$ exactly for every $n$, proving $G\in D(G)$. Therefore the maximum domain of attraction is nonempty exactly when $G$ is max-stable.

For the particular logarithmic tail, complete the distribution function by taking $F(x)=0$ for $x\le x_0$. Let $a_n>1$ be the unique solution of $a_n\log a_n=n$, and take $b_n=0$. Equivalently $F(a_n)=1-1/n$, including $n=1$ with $a_1=x_0$. For each fixed $x>0$, eventually $a_{n}x>x_0$, andHere $a_n\to \infty$ follows directly from its defining equation. Since $\log (1-z)=-z+O(z^2)$ as $z\to 0$, it follows that $n\log F(a_{n}x)\to -1/x$. For $x\le 0$, the probability is zero. ConsequentlyThis is the shape-one Fréchet distribution, and the calculation proves the limit at every real $x$ without needing a regular variation theorem.

The function $t\log t$ is strictly increasing on $t>1$. For every integer $n\ge 3$, $n\log n>n=a_n\log a_n$, so $a_n<n$. The defining equation now gives $a_n=n/\log a_n>n/\log n$. Taking logarithms yields $\log a_n>\log n-\log \log n$, whose right side is positive, soMultiplying by $\log n/n$ and squeezing gives $a_n\log n/n\to 1$. Slutsky theorem applied to the normalized sample maximum therefore gives the simpler logarithmic-tail Fréchet normalization

For large $t$, write the survival function asA positive function $\ell$ is slowly varying if $\ell (tu)/\ell (t)\to 1$ for every fixed $u>0$. Heresince $\log (tu)=\log t+\log u$ and $\log \log (tu)-\log \log t\to 0$. Thus $\overline{F}$ has regular variation of index $-2$.

The relevant extreme-value criterion is that an infinite right endpoint and a survival function regularly varying with index $-r$, $r>0$, give attraction to the standard Fréchet distribution of shape $r$. Its distribution function is $G_r(x)=e^{-x^{-r}}$ for $x>0$ and zero for $x\le 0$. Here is a direct verification, so no additional theorem is needed to establish the criterion in this example. Choose $a_n\to \infty$ satisfying $n\overline{F}(a_n)\to 1$ and set $b_n=0$. For $x>0$,If $u_n\to 0$ and $n{u}_n\to c$, then $n\log (1-u_n)=-n{u}_n+O(n{u}_n^2)\to -c$. Applying this with $u_n=\overline{F}(a_{n}x)$ gives $F(a_{n}x{)}^n\to e^{-x^{-2}}$. For $x\le 0$, the distribution function is zero because the observations are supported on $[10,\infty )$. ThereforeFor example, elementary asymptotic normalizers arefor sufficiently large $n$, with arbitrary positive definitions at smaller indices. Indeed $\log a_n\sim \frac{1}{2}\log n$ and $\log \log a_n\sim \log \log n$, so $a_n^2\log a_n\log \log a_n\sim Cn$. The precise valid choice of $C$ at the lower endpoint does not change the limiting shape.

A non-degenerate probability distribution is not concentrated at a single point. In extreme value theory, a non-degenerate distribution function $F$ is max-stable if, for each integer $n\ge 1$, constants $a_n>0,b_n\in \mathbb{R}$ satisfy $F(a_{n}x+b_n{)}^n=F(x)$ for every $x$. This says that a normalized sample maximum has the same law as one observation. A distribution function $F$ belongs to the maximum domain of attraction of a non-degenerate $G$ if there are $a_n>0,b_n$ such thatat every continuity point of $G$. Equivalently, $(M_n-b_n)/a_n$ has convergence in distribution to $G$.

The extremal types theorem says that any such non-degenerate limit is max-stable and, up to a positive affine change of variable, is exactly one of the following:These are respectively the Gumbel distribution, Fréchet distribution, and negative Weibull distribution. The theorem is also known as the Fisher–Tippett–Gnedenko theorem.

Useful sufficient conditions can be expressed using the survival function $\overline{F}=1-F$ and the right endpoint of a distribution $x_F$. An infinite endpoint with $\overline{F}(tx)/\overline{F}(t)\to x^{-\alpha }$ for every $x>0$ gives the Fréchet distribution domain. A finite endpoint with $\overline{F}(x_F-sx)/\overline{F}(x_F-s)\to x^{\alpha }$ as $s\downarrow 0$ gives the negative Weibull distribution domain. For the Gumbel distribution, it suffices that a positive Gumbel auxiliary function $a(t)$ gives $\overline{F}(t+a(t)x)/\overline{F}(t)\to e^{-x}$ for every real $x$ as $t\uparrow x_F$. These are regular variation and exponential tail-ratio conditions; they need no proof here.

In case (i), ${\overline{F}}_1(x)=(1-x{)}^2$ on $0<x<1$, so the finite-endpoint ratio is exactly $x^2$. Therefore the domain is negative Weibull with shape $2$. With $a_n=n^{-1/2}$ and $b_n=1$,and the limit is one for $x\ge 0$.

In case (ii), ${\overline{F}}_2(t)=e^{-\lambda t}$ has exponential tail ratio with the constant Gumbel auxiliary function $1/\lambda$. Therefore the domain is Gumbel. Choosing $a_n=1/\lambda$ and $b_n=\log n/\lambda$ givesIn case (iii), ${\overline{F}}_3(t)=(1+t{)}^{-\lambda }$ has regular variation of index $-\lambda$, so the domain is Fréchet with shape $\lambda$. The convenient choices $a_n=n^{1/\lambda }$ and $b_n=-1$ givewith limit zero for $x\le 0$.

Finally, define the empirical distribution function $F_n(x)=n^{-1}{\sum }_{i=1}^n{1}_{\{X_i\le x\}}$. On a sample with positive threshold $t=X_{(n-k)}$ and exactly $k$ observations strictly above $t$, its survival function has $1-F_n(t)=k/n$. Using the Tonelli theorem to integrate the finite nonnegative sum givesThis is the Hill estimator as an empirical plug-in version of the tail-integral limit. It does not prove consistency for fixed $k$; that is a separate issue.

There is a finite-sample qualification because the question permits ties and does not assume positive observations. The formula requires $1\le k<n$ and $t>0$. If $r=\#\{i:X_i>t\}<k$ because the threshold is tied, direct substitution giveswhere terms equal to the threshold contribute zero. This is generally $k/r$ times the displayed Hill estimator, rather than the same estimator. If $r=0$, the empirical plug-in denominator is zero. For a continuous probability distribution the no-tie condition holds almost surely; for a Fréchet distribution domain and fixed $k$, the threshold is positive with probability tending to one. Thus the stated plug-in identity holds at a positive untied threshold, and the literal claim for all samples from an arbitrary distribution function needs this qualification.

For independent and identically distributed random variables with distribution function $F$, let $M_n={\max }_{1\le i\le n}{X}_i$ be the sample maximum. In extreme value theory, $F$ belongs to the maximum domain of attraction $D(G)$ of a nondegenerate distribution function $G$ when there exist $a_n>0$ and $b_n\in \mathbb{R}$ such thatat every continuity point of $G$. Thus the normalized sample maxima have convergence in distribution to $G$.

A positive measurable function $q$ has regular variation at infinity with index $\rho$, written $q\in {\mathrm{RV}}_{\rho }$, ifThe case $\rho =0$ defines a slowly varying function; equivalently a regularly varying function is $q(t)=t^{\rho }\ell (t)$ with $\ell$ a slowly varying function.

Write $\overline{F}=1-F$ for the survival function and $x_F=\sup \{x:F(x)<1\}$ for the right endpoint of a distribution. For $\alpha >0$, use the following standard extreme value theory normalization conventions:They are respectively the Fréchet distribution, the negative Weibull distribution, and the Gumbel distribution for maxima. The negative Weibull distribution is supported to the left of its finite endpoint; it is not the usual positive Weibull distribution.

The necessary and sufficient maximum domain of attraction criteria are:

For the corresponding sufficient hazard function conditions, assume $F$ is absolutely continuous near $x_F$, its survival function tends to zero there, and its probability density function $f$ is positive there. Write $\eta (t)=f(t)/\overline{F}(t)$. The Von Mises conditions for extreme values giveThe last condition additionally assumes that the reciprocal hazard function is continuously differentiable near the endpoint. It concerns the derivative of the reciprocal hazard function, not the derivative of the hazard function itself. Equivalently, when $f$ is differentiable, it is $\overline{F}(t)f^{\prime }(t)/f(t{)}^2\to -1$.

To see the connection, taking an integral of the hazard function givesThe first two limits make the logarithmic tail ratios over multiplicative or endpoint-distance intervals tend to $-\alpha \log u$ or $\alpha \log u$, giving the required regular variation. For the Gumbel distribution, set $a(t)=1/\eta (t)$. The derivative condition makes $a(t+a(t)v)/a(t)\to 1$ uniformly on bounded $v$-intervals, soThe endpoint assumptions ensure these shifts are admissible: at an infinite endpoint $a(t)/t\to 0$, and at a finite endpoint $a(t)/(x_F-t)\to 0$. These are sufficient smooth-tail conditions; the tail-ratio characterizations above are necessary and sufficient without differentiability assumptions. For a source using the same reciprocal-hazard convention, see Smith and Weissman's university lecture notes, equations (2.18)–(2.24).

For the Gamma distribution with integer shape $m$ and rate one, let ${\overline{F}}_m(t)={\int }_t^{\infty }{u}^{m-1}{e}^{-u}du/(m-1)!$. The base case is ${\overline{F}}_1(t)=e^{-t}$, and integration by parts givesInduction provesThis survival function has the asymptotic form ${\overline{F}}_m(t)\sim e^{-t}{t}^{m-1}/(m-1)!$; for $m=1$ the relation is exact. Put $t_n={\beta }_n+x$. For every fixed real $x$, $t_n\to \infty$ and $t_n/\log n\to 1$. The proposed centering satisfiessoOnly the highest-degree term contributes to the limit, with the same reasoning covering $m=1$. The Taylor expansion $\log (1-u)=-u+O(u^2)$ now givesbecause $n{\overline{F}}_m({\beta }_n+x{)}^2\to 0$. Therefore the Gumbel limit for gamma maxima isEquivalently, $M_n-{\beta }_n$ has convergence in distribution to the standard Gumbel distribution, with scale $a_n=1$.

A power law is a relation $f(x)=C{x}^p$, with fixed constants $C$ and $p$. For positive $x$, any real number $p$ is allowed. Its useful feature is a simple scaling rule:The same proportional change of input produces the same proportional change of output, independently of the starting value. The exponent controls that response; the coefficient fixes the magnitude.

For example, the area of a circle is $A(r)=\pi r^2$. Doubling the radius multiplies its area by four, and tripling it multiplies the area by nine. The volume of a sphere is $V(r)=4\pi r^3/3$: the same changes multiply its volume by eight and twenty-seven. The distinction explains why similarly shaped objects do not preserve their ratio of surface area to volume when their size changes.

When the output is positive, a logarithm turns the power law into a straight-line relation. Using positive reference values $x_0$ and $f_0=f(x_0)$,These ratios are dimensionless quantities, so the formula also applies to physical measurements with units. Plotting both ratios on a logarithmic scale gives a line of slope $p$. Changing a unit changes the numerical coefficient, while the exponent stays the same. Positive $p$ describes increasing values; negative $p$ describes decreasing values; $p=0$ gives a constant. The zero coefficient is a degenerate case with no useful logarithmic slope.

Exact scaling is stronger than regular variation. The function $x^2\log x$ has a doubling ratio tending to four, but its logarithmic correction keeps growing. The diagram contrasts it with the exact power law $x^2$.

A power law for a probability density and a power law for a tail probability have different exponents. For a Pareto distribution with $\alpha >0$ and lower endpoint one,The tail probability has exponent $-\alpha$; differentiating it and changing sign gives the probability density exponent $-\alpha -1$. Confusing them changes the interpretation of a fitted exponent. The normalizing coefficient and lower endpoint also matter: a decreasing power law is not automatically a valid probability density over the entire positive real line.

The appearance of a line over a finite logarithmic range is evidence to investigate, rather than a proof of an exact power law or its mechanism. In their study of power-law distributions, Clauset, Shalizi and Newman combine maximum likelihood estimation, goodness-of-fit tests and comparisons with alternative models; their abstract explains why ordinary least-squares fitting can give misleading results for tail data. See Aaron Clauset, Cosma Rohilla Shalizi and M. E. J. Newman, Power-law distributions in empirical data, SIAM Review 51 (2009), 661–703. The scaling rule is mathematically exact when it defines a model; whether a dataset supports that model is a question of statistical inference.

A power law $q(t)=t^{\rho }$ responds to every fixed rescaling $t\mapsto ut$ by the same factor $u^{\rho }$. Regular variation keeps this scaling behaviour in the limit $t\to \infty$, while allowing departures from an exact power law. It is useful when the scaling exponent is stable but a smaller correction continues changing. The correction can even grow without bound.

An eventually positive measurable function $q$ has regular variation at infinity with index $\rho \in \mathbb{R}$ ifThe argument $ut$ is in the domain of a function for sufficiently large $t$. Each $u$ is held fixed as the limit is taken; allowing $u$ to change with $t$ is a different assertion. This is the scalar definition recalled by ^[ref]. The index is unique: taking $u=2$ determines $2^{\rho }$.

For example, on $t>1$ the function $q(t)=t^2\log t$ hasThus $q$ has regular variation of index two. Yet $q(t)/t^2=\log t\to \infty$: regular variation does not require asymptotic equivalence to a constant times a power law. Doubling $t$ gives $q(2t)/q(t)=4(1+\log 2/\log t)$. At $t=100,10^4,10^6$ this is approximately $4.60206,4.30103,4.20069$, respectively. The ratio approaches four slowly, even though the exponent is exactly two in the limiting scaling law.

The slowly varying function isolates this correction. Set $\ell (t)=q(t)/t^{\rho }$. ThenConversely, multiplying a slowly varying function by $t^{\rho }$ gives regular variation of index $\rho$. Hence the decomposition $q(t)=t^{\rho }\ell (t)$ is exact on its eventual domain of a function; it is the scaling relation, rather than the decomposition itself, that is asymptotic.

A Pareto distribution gives a decreasing example with a direct interpretation. For $x\ge 1$, take the tail probability $\mathbb{P}(X>x)=x^{-2}$. Its regular variation index is $-2$. Doubling a threshold divides its tail probability by four, andFor a tail probability with regular variation of index $-\alpha$, the same conditional probability tends to $2^{-\alpha }$. An exponential distribution behaves differently: its corresponding conditional probability is $e^{-\lambda t}\to 0$. This comparison explains why scaling limits are useful in extreme value theory; it does not establish that an observed dataset follows a particular tail model. The role of regular variation in heavy-tailed distributions is discussed in Lindskog, Resnick and Roy, introduction, p. 1.

An exponential function $q(t)=e^t$ is not regularly varying with any finite index: at $u=2$, $q(2t)/q(t)=e^t\to \infty$, rather than $2^{\rho }$. Boundedness alone is also insufficient, as the oscillating counterexample in slowly varying function shows. Checking a few rescaling ratios at finite arguments can illustrate a proposed law, but cannot prove its limiting definition.

Jovan Karamata developed this theory in his 1930 paper and returned to its fundamental results in 1933. The opening of his 1933 paper identifies the earlier work; pp. 57–58 connect the rescaling limit with a power law and a slowly varying factor. See J. Karamata, Sur un mode de croissance régulière. Théorèmes fondamentaux, Bulletin de la Société Mathématique de France 61 (1933), 55–62. This gives a precise language for “almost a power” without discarding corrections that remain large.

A positive measurable function $\ell$ is slowly varying at infinity iffor every fixed $u>0$. It has regular variation of index zero. “Slowly” concerns relative change under multiplication of the argument; it does not mean that the function must be bounded, have a small derivative, or converge to a finite value.

Every positive constant is a slowly varying function. So are $\log t$, $1/\log t$ and $(\log t{)}^a$ for any fixed real number $a$, on $t>1$. Indeed,The first grows without bound, the second tends to zero, and the constant does neither. Another unbounded example is $\ell (t)=e^{\sqrt{\log t}}$:This function can grow much faster than $\log t$ while retaining regular variation of index zero.

Even convergence is unnecessary. The bounded functionoscillates between $e^{-1}$ and $e$. It is nevertheless a slowly varying function: the change in the argument of the sine under $t\mapsto ut$ isand the Lipschitz continuity of the sine makes the logarithm of the ratio tend to zero. The sequences $t_k=\exp (\exp (\pi /2+2\pi k))$ and $s_k=\exp (\exp (3\pi /2+2\pi k))$ give the two values $e$ and $e^{-1}$ for every nonnegative integer $k$, so there is no limit at infinity.

Contrast this with $h(t)=2+\sin (\log t)$, which is positive and bounded but is not a slowly varying function. Fix $u=e^{\pi /2}$. Along $t_k=e^{2\pi k}$ its rescaling ratio is $h(u{t}_k)/h(t_k)=3/2$; along $s_k=e^{\pi +2\pi k}$ the ratio is $1/2$. The persistent oscillations prevent even a limit for this one fixed rescaling. It is not regularly varying with any finite index either.

The useful distinction is whether a fixed proportional change of input eventually produces a negligible proportional change of output. A slowly varying function can therefore preserve the scaling exponent of a power law while changing its magnitude substantially. The decomposition in regular variation explains why a logarithmic factor can matter in numerical estimates without changing the limiting exponent.