Skip to main content
Log in

A Wavelet Characterization for the Upper Global Hölder Index

  • Published:
Journal of Fourier Analysis and Applications Aims and scope Submit manuscript

Abstract

The upper Hölder index has been introduced to describe smoothness properties of a continuous function. It can be seen as the irregular counterpart of the usual Hölder index and has been used to investigate the behavior at the origin of the modulus of smoothness in many classical cases.

In this paper, we prove a characterization of the upper Hölder index in terms of wavelet coefficients. This result is a first step in the estimation of this exponent using wavelet methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Adler, R.J.: The Geometry of Random Field. Wiley, New York (1981)

    Google Scholar 

  2. Berman, S.M.: Gaussian sample functions: uniform dimension and Hölder conditions nowhere. Nagoya Math. J. 46, 63–86 (1972)

    MathSciNet  MATH  Google Scholar 

  3. Berman, S.M.: Local nondeterminism and local times of Gaussian processes. Indiana Univ. Math. J. 23(1), 69–86 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  4. Bousch, T., Heurteaux, Y.: On oscillations of Weierstrass-type functions. Manuscript (1999)

  5. Bousch, T., Heurteaux, Y.: Caloric measure on domains bounded by Weierstrass type graphs. Ann. Acad. Sci. Fenn. Math. 25(2), 501–522 (2000)

    MathSciNet  MATH  Google Scholar 

  6. Bardet, J.M., Bertrand, P.: Definition, properties and wavelet analysis of multiscale fractional Brownian motion. Fractals 15(1), 73–87 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  7. Benassi, A., Bertrand, P., Cohen, S., Istas, J.: Identification of the Hurst index of a step fractional Brownian motion. Stat. Inference Stoch. Process. 3(1), 101–111 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  8. Clausel, M.: Lacunary fractional Brownian motion. To appear in ESAIMPS (2010)

  9. Clausel, M., Nicolay, S.: Wavelet techniques for pointwise anti-Hölderian irregularity. Constr. Approx. 33(1), 41–75 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  10. Clausel, M., Nicolay, S.: Some prevalent results about strongly monoHölder functions. Nonlinearity 23(9), 2101–2116 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  11. Coeurjolly, J.F.: Estimating the parameters of a fractional Brownian motion by discrete variations of its sample paths. Stat. Inference Stoch. Process. 4, 199–227 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  12. Coeurjolly, J.F.: Hurst exponent estimation of locally self-similar Gaussian processes using sample quantiles. Ann. Stat. 36, 1404–1434 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  13. Daubechies, I.: Orthonormal bases of compactly supported wavelets. Commun. Pure Appl. Math. 41, 909–996 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  14. Daubechies, I.: Ten Lectures on Wavelets. SIAM, Philadelphia (1992)

    Book  MATH  Google Scholar 

  15. Flandrin, P.: Wavelet analysis of fractional Brownian motion. IEEE Trans. Inf. Theory 38(2), 910–917 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  16. Heurteaux, Y.: Weierstrass function with random phases. Trans. Am. Math. Soc. 355(8), 3065–3077 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  17. Heurteaux, Y.: Weierstrass function in Zygmund’s class. Proc. Am. Math. Soc. 133(9), 2711–2720 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  18. Istas, J., Lang, G.: Quadratic variations and estimation of the Hölder index of a Gaussian process. Ann. Inst. Poincaré Probab. Stat. 33, 407–436 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  19. Jaffard, S.: Construction et propriétés des bases d’ondelettes. Remarques sur la controlabilité exacte. Ph.D. thesis (1989)

  20. Jaffard, S.: Wavelet techniques in multifractal analysis, fractal geometry and applications. In: Proc. Symp. Pure Math. AMS, Providence (2004)

    Google Scholar 

  21. Jaffard, S., Meyer, Y.: Wavelets methods for pointwise regularity and local oscillations of functions. Mem. Am. Math. Soc. 123, 587 (1996)

    MathSciNet  Google Scholar 

  22. Kahane, J.P.: Geza Freud and lacunary Fourier series. J. Approx. Theory 46(1), 51–57 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  23. Ken, J.T., Wood, A.T.A.: Estimating the fractal dimension of a locally self-similar Gaussian process using increments. J. R. Stat. Soc. Ser. B 59, 679–700 (1997)

    Google Scholar 

  24. Khintchine, A.: Ein Satz der Wahrscheinlichkeitsrechnung. Fundam. Math. 6, 9–20 (1924)

    Google Scholar 

  25. Krantz, S.G.: Lipschitz spaces, smoothness of functions, and approximation theory. Expo. Math. 3, 193–260 (1983)

    MathSciNet  Google Scholar 

  26. Mallat, S.: A Wavelet Tour of Signal Processing. Academic Press, San Diego (1998)

    MATH  Google Scholar 

  27. Meyer, Y.: Ondelettes et Opérateurs. Hermann, Paris (1990)

    Google Scholar 

  28. Samorodnitsky, G., Taqqu, M.S.: Stable Non-Gaussian Random Processes. Chapman & Hall, London (1994)

    MATH  Google Scholar 

  29. Stoev, S., Taqqu, M., Park, C., Michailidis, G., Marron, J.S.: LASS: a tool for the local analysis of self-similarity. Comput. Stat. Data Anal. 50, 2447–2471 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  30. Weiss, M.: On the law of iterated logarithm for lacunary trigonometric series. Trans. Am. Math. Soc. 91, 444–469 (1959)

    MATH  Google Scholar 

  31. Xiao, Y.: Hölder conditions for the local times and the Hausdorff measure of the level sets of Gaussian random fields. Probab. Theory Relat. Fields 109, 129–157 (1997)

    Article  MATH  Google Scholar 

  32. Xiao, Y.: Properties of local nondeterminism of Gaussian and stable random fields and their applications. Ann. Fac. Sci. Toulouse Math. XV, 157–193 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Samuel Nicolay.

Additional information

Communicated by Stéphane Jaffard.

Appendix: Optimality of the Assumptions of Theorem 3

Appendix: Optimality of the Assumptions of Theorem 3

We prove here the optimality of the assumptions of Proposition 5 and thus of Theorem 3. To this end we use two counter-examples already introduced in [19].

1.1 A.1 A Uniform Irregular Function Satisfying Property (8)

Let α∈(0,1), 0N and define the two following sequences of integers (j n ) nN and (j n,α ) nN as

$$\left\{\begin{array}{l@{\quad}l}j_1=\ell_0,\\[3pt]j_{n+1}=[\frac{1}{1-\alpha}2^{j_n \alpha}-j_n \alpha],& \forall n\geq1,\\[6pt]j_{n,\alpha}={[2^{j_n \alpha}]},& \forall n\geq1.\end{array}\right.$$

We aim at proving the following result.

Proposition 12

Let us assume that the multiresolution analysis is compactly supported. Let ε∈(0,1) and 0 be such that \(\mathrm {supp}(\psi)\subset[-2^{\ell_{0}},2^{\ell_{0}}]\). Furthermore, let us assume that ψ(0)≠0. The function f defined as

satisfies the following properties:

  1. 1.

    f is not a uniformly Hölderian function,

  2. 2.

    the wavelet coefficients of f satisfy property (8),

  3. 3.

    f is uniformly irregular with exponent β, where

    $$ \beta=\max\biggl(\alpha\varepsilon,\frac{\alpha\varepsilon}{(1-\alpha )+\alpha\varepsilon}\biggr)<\alpha.$$
    (27)

Proof

The two first properties being straightforward, we just have to prove that f is uniformly irregular with exponent β. Let nN and define

$$f_j(x)=\sum_{\ell=j+2}^{j_{n,\alpha}} \ell^{-\varepsilon} \psi \big(2^\ell(x-2^{-(j-\ell_0)})\big),$$

for j∈{j n ,…,j n,α } and

$$f_j(x)=\sum_{\ell=j+2}^{j_{n+1}} 2^{-\ell} \ell^{-\varepsilon}\psi\big( 2^\ell(x-2^{-(j-\ell_0)})\big)+\sum_{\ell=j_{n+1}}^{j_{n+1,\alpha}}\ell^{-\varepsilon}\psi\big (2^\ell(x-2^{-(j-\ell_0)})\big),$$

for j∈{j n,α ,…,j n+1−1}. We need to estimate

$$f(2^{-(j-\ell_0)})-f(0) = f(2^{-(j-\ell_0)})$$

for any jN. First, observe that for jj′, supp(f j )∩supp(f j)=∅. Indeed for any j, we have

$$\mathrm{supp}(f_j)\subset[3.2^{-(j+2-\ell_0)},5.2^{-(j+2-\ell_0)}]\;$$

and hence \(f(2^{-(j-\ell_{0})})-f(0)=f_{j}(2^{-(j-\ell_{0})})\) for any jN.

We now distinguish two cases. Let us first assume that j∈{j n ,…,j n,α }; we have

$$f(2^{-(j-\ell_0)})=2^{-j_n \alpha} \sum_{\ell=j+2}^{j_{n,\alpha}} \ell^{-\varepsilon } \psi(0)\ge2^{-j_n \alpha}((j_{n,\alpha}+1)^{1-\varepsilon }-(j+2)^{1-\varepsilon})$$

Therefore, if j n jj n,α /2,

$$f(2^{-(j-\ell_{0})})\ge2^{-j_n \alpha}(j_{n,\alpha}+1)^{1-\varepsilon }(1-2^{-(1-\varepsilon)})\ge C' 2^{-j\alpha\varepsilon},$$

whereas if j n,α /2≤jj n,α ,

$$f(2^{-(j-\ell_{0})})\ge2^{-j_n \alpha}j_{n,\alpha}^{-\varepsilon}\ge j^{-1-\varepsilon}.$$

Gathering these inequalities, we have, for any j∈{j n ,…,j n,α },

$$ f(2^{-(j-\ell_0)})\ge C' 2^{-j\alpha\varepsilon}.$$
(28)

Let us now consider the second case, where j∈{j n,α +1,…,j n+1−1} for some nN. We have

$$f(2^{-(j-\ell_0)})= \Biggl(2^{j_{n+1}(1-\alpha)}\sum_{\ell =j+2}^{j_{n+1}}2^{-\ell}\ell^{-\varepsilon}+ 2^{-j_{n+1}\alpha}\sum _{\ell=j_{n+1}}^{j_{n+1,\alpha}}\ell^{-\varepsilon}\Biggr)\psi(0).$$

If one remarks that

then for any j n,α +1≤j≤((1−α)+αε)j n+1, we get

$$ f(2^{-(j-\ell_{0})})\ge C' 2^{j\frac{1-\alpha}{(1-\alpha)+\alpha\varepsilon}}2^{-j}j^{-\varepsilon}= C' 2^{-j\frac{\alpha\varepsilon}{(1-\alpha)+\alpha\varepsilon }}j^{-\varepsilon},$$
(29)

whereas if ((1−α)+αε)j n+1jj n+1−1,

$$ f(2^{-(j-\ell_0)})\ge C' 2^{-j_{n+1}\alpha\varepsilon}\ge C' 2^{-j\frac{\alpha\varepsilon}{(1-\alpha)+\alpha\varepsilon}}.$$
(30)

Inequalities (28), (29) and (30) together imply fUI β(R d). □

1.2 A.2 Necessity of the Logarithmic Correction in the Wavelet Criteria

Let ε,α∈(0,1), β>1 and define (j n ) nN as

$$j_n={[\beta^n]},$$

for any nN. Let us also define the function f α,β,ε on R as follows,

$$ f_{\alpha,\beta,\varepsilon}(x)=\sum_{n=0}^{\infty} \sum_{j=j_n+1}^{j_{n+1}} \frac{\inf (2^{-j_{n}\alpha},2^{j_{n+1}(1-\alpha)}2^{-j})}{j^\varepsilon} \sin (2^j \pi x).$$
(31)

We first give an estimation of the wavelet coefficients (c j,k ) of f α,β,ε .

Proposition 13

Assume that the multiresolution analysis is the Meyer multiresolution analysis. Then for n≥1, any j∈{j n ,…,j n+1−1} and any C>0,

$$ \sup_{k\in\mathbf{Z}} |c_{j,k}|\le C\inf (2^{-j_n\alpha },2^{j_{n+1}(1-\alpha)}2^{-j}),$$
(32)

for n sufficiently large.

Proof

Let nN and ∈{j n ,…,j n+1−1}. By definition of the wavelet coefficients of a bounded function, we have

$$c_{\ell,k}= 2^{\ell}\int_{\mathbf{R}^d} f_{\alpha ,\beta,\varepsilon}(x)\psi(2^{\ell}x-k) \,dx.$$

Since the trigonometric series f α,β,ε is uniformly converging on any compact,

$$c_{\ell,k}=2^{\ell} \sum_{n=0}^{\infty} \sum_{j=j_n+1}^{j_{n+1}}\frac{\inf(2^{-j_n \alpha},2^{j_{n+1}(1-\alpha )}2^{-j})}{j^\varepsilon} \int_{\mathbf{R}^d} \sin (2^j \pi x)\psi(2^\ell x-k) \,dx,$$

or

that is,

(33)

Since the Meyer wavelet belongs to the Schwartz class, its Fourier transform is symmetric and compactly supported with

$$\mathrm{supp}(\hat{\psi})\subset\biggl[-\frac{8\pi}{3},-\frac{2\pi }{3}\biggr]\cup\biggl[\frac{2\pi}{3},\frac{8\pi}{3}\biggr],$$

the sum in equality (33) contains at most five terms corresponding to

One directly checks that for any nN, j∈{j n ,…,j n+1−1}, this implies inequality (32). □

Let us now prove the uniform irregularity properties of the functions f α,β,ε .

Proposition 14

For any β>1 and any (α,ε)∈(0,1)2, \(f_{\alpha,\beta,\varepsilon}\in \mathit{UI}^{\alpha}_{1-\varepsilon }(\mathbf{R})\).

Proof

Let us remark that it is sufficient to prove that for any N,

$$ f_{\alpha,\beta,\varepsilon} (2^{-\ell})\ge2^{-\alpha\ell} \ell ^{1-\varepsilon}.$$
(34)

Let n 0N and \(\ell\in\{j_{n_{0}+1},\ldots,j_{n_{0}+1}\}\). By definition, we have

The classical inequality sin(x)≥(2/π)x valid for any x∈[0,π/2] leads to the following inequality if \(j_{n_{0}}+1\le\ell\le j_{n_{0}+1}\),

Let t∈(1,β) such that \(\ell=tj_{n_{0}}\), that is \(j_{n_{0}}=\ell/t\). We get

$$f_{\alpha,\beta,\varepsilon}(2^{-\ell})\ge 2\inf(\ell^{1-\varepsilon}2^{-\ell\frac{\alpha}{t}},\ell ^{1-\varepsilon} 2^{-\ell(1-\frac{\beta\ell}{t}+\frac{\alpha \beta\ell}{t})}).$$

Since

$$\sup_{t\in[1,\beta]} \max(\alpha/t,1-\beta\ell/t+\alpha\beta \ell/t)\le\alpha,$$

inequality (34) is satisfied for any N. □

Propositions 13 and 14 together imply the following proposition.

Proposition 15

For any (α,ε,β)∈(0,1)2×(1,+∞), the functions f α,β,ε defined by the relation (31) are uniformly Hölderian, satisfy (8) and belong to \(\mathit{UI}^{\alpha}_{1-\varepsilon }(\mathbf{R})\).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Clausel, M., Nicolay, S. A Wavelet Characterization for the Upper Global Hölder Index. J Fourier Anal Appl 18, 750–769 (2012). https://doi.org/10.1007/s00041-012-9220-y

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00041-012-9220-y

Keywords

Mathematics Subject Classification (2000)

Navigation