An estimator of the stable tail dependence function based on the empirical beta copula

Abstract

The replacement of indicator functions by integrated beta kernels in the definition of the empirical tail dependence function is shown to produce a smoothed version of the latter estimator with the same asymptotic distribution but superior finite-sample performance. The link of the new estimator with the empirical beta copula enables a simple but effective resampling scheme.

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References

  1. Beirlant, J., Goegebeur, Y., Segers, J., Teugels, J.: Statistics of Extremes: Theory and Applications. Wiley, New York (2004)

    Google Scholar 

  2. Beirlant, J., Escobar-Bach, M., Goegebeur, Y., Guillou, A.: Bias-corrected estimation of stable tail dependence function. J. Multivar. Anal. 143, 453–466 (2016)

    MathSciNet  Article  Google Scholar 

  3. Berghaus, B., Segers, J.: Weak convergence of the weighted empirical beta copula process. J. Multivar. Anal. arXiv:1705.06924 (2017)

  4. Berghaus, B., Bücher, A., Volgushev, S.: Weak convergence of the empirical copula process with respect to weighted metrics. Bernoulli 23(1), 743–772 (2017)

    MathSciNet  Article  Google Scholar 

  5. Bücher, A., Dette, H.: Multiplier bootstrap of tail copulas with applications. Bernoulli 19(5A), 1655–1687 (2013)

    MathSciNet  Article  Google Scholar 

  6. Bücher, A., Segers, J., Volgushev, S.: When uniform weak convergence fails: empirical processes for dependence functions and residuals via epi- and hypographs. Ann. Stat. 42(4), 1598–1634 (2014)

    MathSciNet  Article  Google Scholar 

  7. Bücher, A., Jäschke, S., Wied, D.: Nonparametric tests for constant tail dependence with an application to energy and finance. J. Econ. 187(1), 154–168 (2015)

    MathSciNet  Article  Google Scholar 

  8. Coles, S.G., Tawn, J.A.: Modelling extreme multivariate events. J. R. Stat. Soc. Ser. B (Stat Methodol.) 53(2), 377–392 (1991)

    MathSciNet  MATH  Google Scholar 

  9. de Haan, L., Ferreira, A.: Extreme Value Theory: an Introduction. Springer, Berlin (2006)

    Google Scholar 

  10. de Haan, L., Resnick, S.I.: Limit theory for multivariate sample extremes. Z. Wahrscheinlichkeitstheorie Verwandte Geb. 40(4), 317–337 (1977)

    MathSciNet  Article  Google Scholar 

  11. Deheuvels, P.: La fonction de dépendance empirique et ses propriétés. un test non paramétrique d’indépendance. Acad. R. Belg. Bull. Cl. Sci. (5) 65(6), 274–292 (1979)

    MATH  Google Scholar 

  12. Drees, H., Huang, X.: Best attainable rates of convergence for estimators of the stable tail dependence function. J. Multivar. Anal. 64(1), 25–47 (1998)

    MathSciNet  Article  Google Scholar 

  13. Einmahl, J.H.J., de Haan, L., Li, D.: Weighted approximations of tail copula processes with application to testing the bivariate extreme value condition. Ann. Stat. 34(4), 1987–2014 (2006)

    MathSciNet  Article  Google Scholar 

  14. Einmahl, J.H.J., Krajina, A., Segers, J.: An M-estimator for tail dependence in arbitrary dimensions. Ann. Stat. 40(3), 1764–1793 (2012)

    MathSciNet  Article  Google Scholar 

  15. Einmahl, J.H.J., Kiriliouk, A., Segers, J.: A continuous updating weighted least squares estimator of tail dependence in high dimensions. Extremes. https://doi.org/10.1007/s10687-017-0303-7 (2017)

    MathSciNet  Article  Google Scholar 

  16. Fougères, A.-L., de Haan, L., Mercadier, C.: Bias correction in multivariate extremes. Ann. Stat. 43(2), 903–934 (2015)

    MathSciNet  Article  Google Scholar 

  17. Genton, M.G., Ma, Y., Sang, H.: On the likelihood function of Gaussian max-stable processes. Biometrika 98(2), 481–488 (2011)

    MathSciNet  Article  Google Scholar 

  18. Gissibl, N., Klüppelberg, C.: Max-linear models on directed acyclic graphs. To appear in Bernoulli arXiv:1512.07522 [math.PR] (2015)

  19. Huang, X.: Statistics of bivariate extreme values. Ph. D. thesis, Erasmus University Rotterdam, Tinbergen Institute Research Series 22 (1992)

  20. Huser, R., Davison, A.: Composite likelihood estimation for the Brown–Resnick process. Biometrika 100(2), 511–518 (2013)

    MathSciNet  Article  Google Scholar 

  21. Janssen, P., Swanepoel, J., Veraverbeke, N.: Large sample behavior of the bernstein copula estimator. J. Stat. Plan. Inference 142(5), 1189–1197 (2012)

    MathSciNet  Article  Google Scholar 

  22. Joe, H.: Families of min-stable multivariate exponential and multivariate extreme value distributions. Stat. Probab. Lett. 9, 75–81 (1990)

    MathSciNet  Article  Google Scholar 

  23. Kabluchko, Z., Schlather, M., de Haan, L.: Stationary max-stable fields associated to negative definite functions. Ann. Probab. 37(5), 2042–2065 (2009)

    MathSciNet  Article  Google Scholar 

  24. Kiriliouk, A.: Hypothesis testing for tail dependence parameters on the boundary of the parameter space with application to generalized max-linear models. arXiv:1708.07019 [stat.ME]. (2017)

  25. Kojadinovic, I., Yan, J.: Modeling multivariate distributions with continuous margins using the copula R package. J. Stat. Softw. 34(9), 1–20 (2010)

    Article  Google Scholar 

  26. Peng, L., Qi, Y.: Bootstrap approximation of tail dependence function. J. Multivar. Anal. 99, 1807–1824 (2008)

    MathSciNet  Article  Google Scholar 

  27. Pickands, J.: Multivariate extreme value distributions. In: Proceedings of the 43rd Session of the International Statistical Institute, Vol. 2 (Buenos Aires, 1981). With a discussion, vol. 49, pp 859–878, 894–902 (1981)

  28. R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2017)

    Google Scholar 

  29. Ressel, P.: Homogeneous distributions–and a spectral representation of classical mean values and stable tail dependence functions. J. Multivar. Anal. 117(1), 246–256 (2013)

    MathSciNet  Article  Google Scholar 

  30. Ribatet, M: SpatialExtremes: Modelling Spatial Extremes. R package version 2.0-4 (2017)

  31. Sancetta, A., Satchell, S.: The bernstein copula and its applications to modeling and approximations of multivariate distributions. Econ. Theory 20(03), 535–562 (2004)

    MathSciNet  Article  Google Scholar 

  32. Schmidt, R., Stadtmüller, U.: Non-parametric estimation of tail dependence. Scand. J. Stat. 33(2), 307–335 (2006)

    MathSciNet  Article  Google Scholar 

  33. Segers, J., Sibuya, M., Tsukahara, H.: The empirical beta copula. J. Multivar. Anal. 155, 35–51 (2017)

    MathSciNet  Article  Google Scholar 

  34. Sklar, M.: Fonctions de répartition à n dimensions et leurs marges. Université Paris 8 (1959)

  35. Tawn, J.A.: Modelling multivariate extreme value distributions. Biometrika 77(2), 245–253 (1990)

    MathSciNet  Article  Google Scholar 

  36. van der Vaart, A.W., Wellner, J.A.: Weak Convergence and Empirical Processes. Springer, New York (1996)

    Google Scholar 

Download references

Acknowledgments

A. Kiriliouk gratefully acknowledges support from the Fonds de la Recherche Scientifique (FNRS).

J. Segers gratefully acknowledges funding by contract “Projet d’Actions de Recherche Concertées” No. 12/17-045 of the “Communauté française de Belgique” and by IAP research network Grant P7/06 of the Belgian government (Belgian Science Policy).

L. Tafakori would like to thank the Australian Research Council for supporting this work through Laureate Fellowship FL130100039.

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Correspondence to Johan Segers.

Appendix A: Proofs of Propositions 3.5 and 3.6

Appendix A: Proofs of Propositions 3.5 and 3.6

Proof of Proposition 3.5

Fix ε ∈ (0,δ].Since νn,k,xis a probability measure, we can bring the termBn,k(x)inside the integral. Split the integral according to the two cases|yx|ε or |yx| > ε,where |z| = max(|z1| ,…, |zd|)for \(\boldsymbol {z} \in \mathbb {R}^{d}\).For x ∈ [0, 1]d, the absolute value in (3.3) is bounded by

$$\begin{array}{@{}rcl@{}} && \sup \left\{ \left| B_{n,k}(\boldsymbol{y}) - B_{n,k}(\boldsymbol{x}) \right| \; : \; \boldsymbol{y} \in [0, n/k]^{d}, \, \left| \boldsymbol{y} - \boldsymbol{x} \right|_{\infty} \le \varepsilon \right\} \\ &&+ 2 \sup_{\boldsymbol{y} \in [0, n/k]^{d}} \left| B_{n,k}(\boldsymbol{y} ) \right| \cdot \nu_{n,k,\boldsymbol{x}} \left( \{ \boldsymbol{y} \in [0, n/k]^{d} : \left| \boldsymbol{y} - \boldsymbol{x} \right|_{\infty} > \varepsilon \} \right). \end{array} $$
(A.1)

In the first term in (A.1), we have x ∈ [0, 1]d,y ∈ [0,n/k]d, and|yx|εδ, whencey ∈ [0, 1 + δ]d.The supremum is thus bounded by the maximal increment ofBn,kon[0, 1 + δ]dbetween points at adistance at most ε apart,i.e.,

$$\omega(B_{n,k}, \varepsilon ) = \sup \{ \left| B_{n,k}(\boldsymbol{y}_{1}) - B_{n,k}(\boldsymbol{y}_{2}) \right| : \boldsymbol{y}_{1}, \boldsymbol{y}_{2} \in [0, 1+\delta]^{d}, \, \left| \boldsymbol{y}_{1} - \boldsymbol{y}_{2} \right|_{\infty} \le \varepsilon \}. $$

By Condition 3.3, wecan find for every η > 0asufficiently small ε > 0 such that

$$\limsup_{n \to \infty} \mathbb{P} \left[ \omega(B_{n,k}, \varepsilon ) > \eta \right] \le \eta. $$

Thefirst term in (A.1) can thus be made arbitrarily small with arbitrarily large probability, uniformlyin x ∈ [0, 1]dandfor sufficiently large n.

For the second term in (A.1), note first that

$$ \sup_{\boldsymbol{y} \in [0, n/k]^{d}} \left| B_{n,k}(\boldsymbol{y} ) \right| = \mathrm{O}_{\mathbb{P}}(n / \sqrt{k} ), \qquad n \to \infty. $$
(A.2)

Indeed, since isa stdf, we have 0 ≤ (y) ≤ y1 + ⋯ + yddn/k for y ∈ [0,n/k]d; for thepilot estimator \(\hat {\ell }_{n,k}\),use Condition 3.2.

If S is a Bin(n,u) random variable, Bennett’s inequality van der Vaart and Wellner (1996, Proposition A.6.2) states that

$$\mathbb{P}[ \sqrt{n} \left| S/n - u \right| \ge \lambda ] \le 2 \exp \left\{ - nu \, h \left( 1 + \frac{\lambda}{\sqrt{n} u} \right) \right\}, \qquad \lambda > 0, $$

where\(h(1+\eta ) = {\int }_{0}^{\eta } \log (1+t) \,\mathrm {d} t\) forη ≥ 0. Note that\(h(1+\eta ) > \frac {1}{3} \eta ^{2}\) forη ∈ [0, 1]. Itfollows that

$$\begin{array}{@{}rcl@{}} \nu_{n,k,\boldsymbol{x}} \left( \{ \boldsymbol{y} \in [0, n/k]^{d} : \left| \boldsymbol{y} - \boldsymbol{x} \right|_{\infty} > \varepsilon \} \right) &\le& \sum\limits_{j = 1}^{d} \mathbb{P}\left[ \left| \operatorname{Bin}(n, \frac{k}{n} x_{j}) / k - x_{j} \right| >\varepsilon\right]\\ &=& \sum\limits_{j = 1}^{d} \mathbb{P}\left[ \sqrt{n} \left| \operatorname{Bin}(n, \frac{k}{n} x_{j}) / n - \frac{k}{n} x_{j} \right| > k\varepsilon/\sqrt{n}\right]\\ \end{array} $$
(A.3)
$$\begin{array}{@{}rcl@{}} && \le \sum\limits_{j = 1}^{d} 2 \exp \left\{ - kx_{j} \, h \left( 1 + \frac{k\varepsilon/\sqrt{n}}{\sqrt{n} kx_{j}/n} \right) \right\} \\ &&= \sum\limits_{j = 1}^{d} 2 \exp \left\{ - kx_{j} \, h (1 + \varepsilon/x_{j} ) \right\}. \end{array} $$
(A.4)

As {x h(1 + ε/x)}/x < 0for0 < x < 1, we have inf x∈[0,1]{x h(1 + ε/x)} = h(1 + ε). We concludethat

$$\nu_{n,k,\boldsymbol{x}} \left( \!\{ \boldsymbol{y} \in [0, n/k]^{d} : \left| \boldsymbol{y} \,-\, \boldsymbol{x} \right|_{\infty} > \varepsilon \} \right) \le 2d \exp \{ - k \, h(1+\varepsilon) \} \le 2d \exp \!\left( - \frac{1}{3} k \varepsilon^{2} \right)\!. $$

Together, thesupremum over x ∈ [0, 1]dof the second term in (A.1) is of the order

$$ \mathrm{O}_{\mathbb{P}} \left( \frac{n}{\sqrt{k}} \exp \left( - \frac{1}{3} k \varepsilon^{2} \right) \right), \qquad n \to \infty. $$
(A.5)

It therefore converges to zero in probability since log(n) = o(k)byassumption. □

Remark A.1

In Proposition 3.5, the condition log(n) = o(k)was imposed to control the remainder term in (A.5). That term arose from an application ofBennett’s inequality in (A.4), producing an upper bound to the binomial tail probability theline before. We now show that the same probability also admits a lower bound that would yield the same condition on k. Indeed, starting from the left-hand side of (A.3), we have

$$\begin{array}{@{}rcl@{}} \nu_{n,k,\boldsymbol{x}} \left( \{ \boldsymbol{y} \in [0, n/k]^{d} : \left| \boldsymbol{y} - \boldsymbol{x} \right|_{\infty} > \varepsilon \} \right) &\ge& \mathbb{P}\left[ \left| \operatorname{Bin}\left( n, \frac{k}{n} x_{1}\right) / k - x_{1} \right| > \varepsilon \right] \\ &\ge& \mathbb{P}\left[ \operatorname{Bin}\left( n, \frac{k}{n} x_{1}\right) > k(x_{1}+ \varepsilon) \right] \\ &\ge& \mathbb{P}\left[ \operatorname{Bin}\left( n, \frac{k}{n} x_{1}\right) = m \right] \end{array} $$

where m = mn = ⌊k(x1 + ε) + 1⌋and⌊ ⋅ ⌋ is the floor function. Stirling’s formula says that \(n! = \sqrt {2\pi n} (n/e)^{n} \{1 + \mathrm {o}(1)\}\) asn and thus, since k = kn,also

$$\frac{n!}{m!(n-m)!} = (2\pi m)^{-1/2} (n/m)^{m} (1 - m/n)^{-(n-m)} \{1 + \mathrm{o}(1)\}. $$

But then

$$\begin{array}{@{}rcl@{}} \mathbb{P}\left[ \operatorname{Bin}\left( n, \frac{k}{n} x_{1}\right) = m \right] &=& \frac{n!}{m!(n-m)!} \left( \frac{k}{n}x_{1}\right)^{m} \left( 1 - \frac{k}{n}x_{1}\right)^{n-m} \\ &=& \frac{1}{\sqrt{2\pi m}} \left( \frac{kx_{1}}{m} \right)^{m} \left( \frac{1 - kx_{1} / n}{1 - m/n} \right)^{n-m} \{ 1 + \mathrm{o}(1) \} \\ &\ge& \frac{1}{\sqrt{2\pi m}} \left( \frac{kx_{1}}{m} \right)^{m} \{ 1 + \mathrm{o}(1) \}, \end{array} $$

since mkx1. For n sufficiently large, k = knis such that mnk(x1 + 2ε)and thus (kx1/m)mρkwith \(\rho = \{x_{1}/(x_{1}+ 2\varepsilon )\}^{x_{1}+ 2\varepsilon }\). Wefind

$$\mathbb{P}\left[ \operatorname{Bin}\left( n, \frac{k}{n} x_{1}\right) = m \right] \ge \frac{1}{\sqrt{2\pi(x_{1} + 2\varepsilon)}} k^{-1/2} \rho^{k}. $$

Recallthat in the proof of Proposition 3.5, we needed to control the second term in (A.1). Inview of (A.2) and the above lower bound, we need a sequence k such that, for everyε > 0, we have(n/k)ρk → 0. Here0 < ρ = ρ(x1, ε) <  1 approaches 1 as ε 0, for every fixed x1 > 0. Butthen k = knmustbe such that log(n) − log(k) + k log(ρ) →− as n, and since log(ρ) < 0can be arbitrarily close to 0 as ε0,we still need that log(n) = o(k).

Proof of Proposition 3.6

Fix x ∈ [0,M]d.For j ∈{1,…,d}such that xj = 0, the binomial distribution Bin(n, (k/n)xj)is concentrated on 0. As a consequence, the integral overy ∈ [0,n/k]dwith respect to νn,k,xcan be restricted to the set of those y ∈ [0,n/k]dsuch that yj = 0for all j ∈{1,…,d}for which xj = 0.Call this set \(\mathbb {D}(n,k,\boldsymbol {x})\).

For \(\boldsymbol {y} \in \mathbb {D}(n,k,\boldsymbol {x})\), the function\([0, 1] \to \mathbb {R} : t \mapsto f(\boldsymbol {x} + t(\boldsymbol {y}-\boldsymbol {x}))\) is continuouson [0, 1]and is continuously differentiable on (0, 1); indeed, ifxj = 0, then the jth componentof x + t(yx)vanishes and thusdoes not depend on t ∈ [0, 1],while if xj > 0,then that component is (strictly) positive for allt ∈ [0, 1), so that, by assumption,\(\dot {f}_{j}(\boldsymbol {x} + t(\boldsymbol {y}-\boldsymbol {x}))\) exists and iscontinuous in t ∈ [0, 1). WritingJ(x) = {j = 1,…,d : xj > 0}, we find, by the fundamental theorem of calculus,

$$f(\boldsymbol{y} ) - f(\boldsymbol{x} ) = \sum\limits_{j \in J(\boldsymbol{x})} (y_{j} - x_{j}) {{\int}_{0}^{1}} \dot{f}_{j} \left( \boldsymbol{x} + t(\boldsymbol{y} - \boldsymbol{x}) \right) \,\mathrm{d} t, \qquad \boldsymbol{y} \in \mathbb{D}(n,k,\boldsymbol{x}). $$

Weobtain

$$\begin{array}{@{}rcl@{}} {\Delta}_{n,k}(\boldsymbol{x}) &\!:=& {\int}_{\mathbb{D}(n,k,\boldsymbol{x})} \sqrt{k} \{ f(\boldsymbol{y}) - f(\boldsymbol{x}) \} \,\mathrm{d} \nu_{n,k,\boldsymbol{x}}(\boldsymbol{y} )\\ &=& \sum\limits_{j \in J(\boldsymbol{x})} {\int}_{\boldsymbol{y} \in \mathbb{D}(n,k,\boldsymbol{x})} \sqrt{k} (y_{j} - x_{j}) {{\int}_{0}^{1}} \dot{f}_{j} \left( \boldsymbol{x} + t(\boldsymbol{y} - \boldsymbol{x}) \right) \,\mathrm{d} t \,\mathrm{d} \nu_{n,k,\boldsymbol{x}}(\boldsymbol{y}) \\ &=& \sum\limits_{j \in J(\boldsymbol{x})} {\int}_{\boldsymbol{y} \in \mathbb{D}(n,k,\boldsymbol{x})} \sqrt{k} (y_{j} \,-\, x_{j}) {{\int}_{0}^{1}} \left\{ \dot{f}_{j} \!\left( \boldsymbol{x} \,+\, t(\boldsymbol{y} \,-\, \boldsymbol{x}) \right) \,-\, \dot{f}_{j}(\boldsymbol{x}) \right\} \,\mathrm{\!d} t \,\mathrm{d} \nu_{n,k,\boldsymbol{x}}(\boldsymbol{y}), \end{array} $$

where the last step is justified via \(\int y_{j} \,\mathrm {d} \nu _{n,k,\boldsymbol {x}}(\boldsymbol {y}) = \operatorname {\mathbb {E}}[ \operatorname {Bin}(n, (k/n)x_{j})/k ] = x_{j}\).Taking absolute values, we find, forx ∈ [0,M]d,

$$\left| {\Delta}_{n,k}(\boldsymbol{x}) \right| \le \sum\limits_{j \in J(\boldsymbol{x})} I_{n,k}(\boldsymbol{x}, j) $$

where

$$I_{n,k}(\boldsymbol{x}, j) = {\int}_{\boldsymbol{y} \in \mathbb{D}(n,k,\boldsymbol{x})} \sqrt{k} \left| y_{j} - x_{j} \right| {{\int}_{0}^{1}} \left| \dot{f}_{j} \left( \boldsymbol{x} + t(\boldsymbol{y} \,-\, \boldsymbol{x}) \right) \,-\, \dot{f}_{j}(\boldsymbol{x} ) \right| \,\mathrm{d} t \,\mathrm{d} \nu_{n,k,\boldsymbol{x}}(\boldsymbol{y}). $$

We will find anupper bound for In,k(x,j).

Let K > 0be suchthat \(\left | \dot {f}_{i} \right | \le K\) forall i ∈{1,…,d}.Choose δ ∈ (0,M]and ε ∈ (0,δ/2]. InIn,k(x,j), split the integral over\(\boldsymbol {y} \in \mathbb {D}(n,k,\boldsymbol {x})\) into two pieces,depending on whether |yx| ≤ ε or |yx| > ε, where\(\left | \boldsymbol {z} \right | = ({z_{1}^{2}} + {\cdots } + {z_{d}^{2}})^{1/2}\) denotesthe Euclidean norm of \(\boldsymbol {z} \in \mathbb {R}^{d}\).

In In,k(x,j), the integralover \(\boldsymbol {y} \in \mathbb {D}(n,k,\boldsymbol {x})\) for which |yx| > ε is bounded by

$$\begin{array}{@{}rcl@{}} 2K \sqrt{k} {\int}_{\mathbb{D}(n,k,\boldsymbol{x})} \left| y_{j} - x_{j} \right| \operatorname{\mathbb{1}}\{ \left| \boldsymbol{y} - \boldsymbol{x} \right| > \varepsilon \} \,\mathrm{d} \nu_{n,k,\boldsymbol{x}}(\boldsymbol{y}) &\le& \frac{2K\sqrt{k}}{\varepsilon} {\int}_{\mathbb{D}(n,k,\boldsymbol{x})} \left| \boldsymbol{y} - \boldsymbol{x} \right|^{2} \,\mathrm{d} \nu_{n,k,\boldsymbol{x}}(\boldsymbol{y}) \\ &=& \frac{2K\sqrt{k}}{\varepsilon} \sum\limits_{i = 1}^{d} \frac{1}{k^{2}} \cdot n \cdot \frac{k}{n} x_{i} \cdot \left( 1 - \frac{k}{n} x_{i}\right) \\ &\le& \frac{2KMd}{\varepsilon \sqrt{k}}. \end{array} $$

To analyze the integral in In,k(x,j)over those \(\boldsymbol {y} \in \mathbb {D}(n,k,\boldsymbol {x})\) forwhich |yx| ≤ ε, we need to distinguishbetween two cases: xj < δ and xjδ. Incase xj < δ,the integral is simply bounded by

$$\begin{array}{@{}rcl@{}} 2K {\int}_{\mathbb{D}(n,k,\boldsymbol{x})} \sqrt{k} \left| y_{j} - x_{j} \right| \,\mathrm{d} \nu_{n,k,\boldsymbol{x}}(\boldsymbol{y}) &\le& 2K \sqrt{k} \sqrt{ \frac{1}{k^{2}} \cdot n \cdot \frac{k}{n}x_{j} \cdot \left( 1 - \frac{k}{n}x_{j}\right)} \\ &\le&K \sqrt{x_{j}} < 2K \sqrt{\delta}. \end{array} $$

In case xjδ, the inequality |yx| ≤ εδ/2 and the fact that x ∈ [0,M]d and y ∈ [0, )d imply that y belongs to theset

$$\mathbb{B}_{j}(M, \delta) = \{ \boldsymbol{z} \in [0, M+\delta/2]^{2} : z_{j} > \delta/2 \}. $$

Let

$$\omega_{j}(M, \delta, \varepsilon) = \sup \{ \left| \dot{f}_{j}(\boldsymbol{z}_{1} ) - \dot{f}_{j}(\boldsymbol{z}_{2} ) \right| : \boldsymbol{z}_{1}, \boldsymbol{z}_{2} \in \mathbb{B}_{j}(M, \delta), \, \left| \boldsymbol{z}_{1} - \boldsymbol{z}_{2} \right| \le \varepsilon \}. $$

The integralin In,k(x,j)over\(\boldsymbol {y} \in \mathbb {D}(n,k,\boldsymbol {x})\) suchthat |yx| ≤ ε is bounded by

$$\omega_{j}(M, \delta, \varepsilon) {\int}_{\mathbb{D}(n,k,\boldsymbol{x})} \sqrt{k} \left| y_{j} \,-\, x_{j} \right| \,\mathrm{d} \nu_{n,k,\boldsymbol{x}}(\boldsymbol{y}) \!\le\! \omega_{j}(M, \delta, \varepsilon) \sqrt{x_{j}} \le \omega_{j}(M, \delta, \varepsilon) \sqrt{M} $$

using the Cauchy–Schwarz inequality and the first two moments of the binomial distribution.

Assembling all the pieces, we obtain

$$\sup_{\boldsymbol{x} \in [0, M]^{d}} \left| {\Delta}_{n,k}(\boldsymbol{x}) \right| \\ \le \frac{2d^{2}KM}{\varepsilon \sqrt{k}} + 2dK \sqrt{\delta} + \sqrt{M} \sum\limits_{j = 1}^{d} \omega_{j}(M, \delta, \varepsilon). $$

As a consequence, for every δ ∈ (0,M] and every ε ∈ (0,δ/2], wehave

$$\limsup_{n \to \infty} \sup_{\boldsymbol{x} \in [0, M]^{d}} \left| {\Delta}_{n,k}(\boldsymbol{x}) \right| \le 2dK \sqrt{\delta} + \sqrt{M} \sum\limits_{j = 1}^{d} \omega_{j}(M, \delta, \varepsilon). $$

The function\(\dot {f}_{j}\) is continuous and thus uniformly continuous on the compact set \(\mathbb {B}_{j}(M, \delta )\). As consequence, inf ε> 0ωj(M,δ,ε) = 0.The limit superior in the previous display is thus bounded by\(2dK \sqrt {\delta }\), for allδ ∈ (0,M], and must thereforebe equal to zero. □

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Kiriliouk, A., Segers, J. & Tafakori, L. An estimator of the stable tail dependence function based on the empirical beta copula. Extremes 21, 581–600 (2018). https://doi.org/10.1007/s10687-018-0315-y

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Keywords

  • Bernstein polynomial
  • Brown–Resnick process
  • Bootstrap
  • Copula
  • Empirical process
  • Max-linear model
  • Tail copula
  • Tail dependence
  • Weak convergence

Mathematics Subject Classification (2010)

  • 62G32
  • 62G30