2013
DOI: 10.1090/conm/601/11919
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Local Multifractal Analysis

Abstract: Abstract. We introduce a local multifractal formalism adapted to functions, measures or distributions which display multifractal characteristics that can change with time, or location. We develop this formalism in a general framework and we work out several examples of measures and functions where this setting is relevant.

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Cited by 7 publications
(5 citation statements)
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“…In [1], a local spectrum for functions is defined in order to study their Hölder regularity. After the completion of our work, the paper [2] appeared. The paper deals with local multifractal analysis in Euclidean spaces for functions, measures and distributions.…”
Section: Introductionmentioning
confidence: 99%
“…In [1], a local spectrum for functions is defined in order to study their Hölder regularity. After the completion of our work, the paper [2] appeared. The paper deals with local multifractal analysis in Euclidean spaces for functions, measures and distributions.…”
Section: Introductionmentioning
confidence: 99%
“…As was pointed out in [10, Definition 3, Lemma 4] (see also the remark below Definition 4 and Proposition 2 in [9]), the pointwise spectrum is well-defined in the sense that it does not depend on the sequence of open intervals chosen, and the local spectrum D f (A, h) on any open set A can be completely recovered from the pointwise spectrum. More precisely, for any open set A ⊂ R + and any h ≥ 0, we have…”
Section: Introduction and Main Resultsmentioning
confidence: 88%
“…It is thus relevant to consider the pointwise multifractal spectrum at a given point. Other examples with varying pointwise spectrum are studied in [19,9,6]. Definition 1.3.…”
Section: Introduction and Main Resultsmentioning
confidence: 99%
“…Note that (13) states that the wavelet divergence spectrum is locally invariant inside A (see [3] for a precise definition and the basic properties of the related notion of local spectrum). Remark also that if p = +∞, then this definition boils down to the condition: ∀x ∈ A, δ C (x) = −s.…”
Section: Maximally Divergent Wavelet Seriesmentioning
confidence: 99%