1997
DOI: 10.1007/s004400050128
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Hölder conditions for the local times and the Hausdorff measure of the level sets of Gaussian random fields

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Cited by 144 publications
(177 citation statements)
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“…To prove the above integral is finite, we observe that for any 0 < γ < min{d, N j =1 1 33) where N j =N+1 1 H j := 0. In the following, we will only consider the case of k = 1, the remaining cases are simpler because they require less steps of integration using Lemma 9.…”
Section: Note That For Anymentioning
confidence: 99%
See 1 more Smart Citation
“…To prove the above integral is finite, we observe that for any 0 < γ < min{d, N j =1 1 33) where N j =N+1 1 H j := 0. In the following, we will only consider the case of k = 1, the remaining cases are simpler because they require less steps of integration using Lemma 9.…”
Section: Note That For Anymentioning
confidence: 99%
“…Related to these problems, we mention that the Hausdorff measure functions for the range and graph of an (N, d)-fractional Brownian motion X have been obtained by Talagrand [29] and Xiao [33,34]; and the exact packing measure functions for X([0, 1] N ) have been studied by Xiao [32,35]. Their methods are useful for studying the fractional Brownian sheet B H as well.…”
Section: Theorem 5 Let B H = {B H (T) T ∈ Rmentioning
confidence: 99%
“…Pitt (1978)]. As shown by Xiao (1997bXiao ( , 2007 and Shieh and Xiao (2006), many sample path properties of such Gaussian random fields are similar to those of fractional Brownian motion.…”
Section: Remark 33mentioning
confidence: 92%
“…Sample path properties of such Gaussian random fields have been studied widely. See Xiao (1997bXiao ( , 2007, Shieh and Xiao (2006) Many Gaussian random fields can be constructed by choosing the spectral measures appropriately. For example, if we consider the spectral density …”
Section: Anisotropic Gaussian Random Fields With Stationary Incrementsmentioning
confidence: 99%
“…Consider x < x 0 , λ = x 0 /x > 1. The self-similarity of ξ(x) yields P (G(0, 1) ∈ dx) = P (G(0, λ) ∈ λdx) ≤ P (G(0, 1) ∈ λdx) = ψ(x 0 ) x 0 x dx (22) Here, G(a, b) is the leftmost position of the supremum of {ξ(x), x ∈ (a, b)}. Consequently, the distribution of G(0, 1) is absolutely continuous in (0, x 0 ).…”
Section: Appendixmentioning
confidence: 99%