2015
DOI: 10.1016/j.jmaa.2015.05.061
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Estimates of transition densities and their derivatives for jump Lévy processes

Abstract: We give upper and lower estimates of densities of convolution semigroups of probability measures under explicit assumptions on the corresponding Lévy measure and the Lévy-Khinchin exponent. We obtain also estimates of derivatives of densities.

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Cited by 65 publications
(135 citation statements)
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“…where k 1 , k 2 ∈ N 2 . Hence, by Hölder inequality, (21), (10), (15) and (20), for p > 2 α−1 , we get…”
Section: Gradient Estimatesmentioning
confidence: 86%
“…where k 1 , k 2 ∈ N 2 . Hence, by Hölder inequality, (21), (10), (15) and (20), for p > 2 α−1 , we get…”
Section: Gradient Estimatesmentioning
confidence: 86%
“…In this subsection, we will combine some results in [KR16,KS15,KS17] to prove Proposition 3.1. Recall that we have assumed that ν :…”
Section: Settingsmentioning
confidence: 99%
“…Now we are going to establish heat kernel estimates for the process Y obtained in Lemma 3.4, which will imply heat kernel estimate and gradient estimate of X as a consequence of (3.14). To do this, we will check conditions (E), (D), (P) and (C) (when β < 1) in [KS17] for the process X and Y , and apply [KS17,Theorem 4] and [KS15,Theorem 1].…”
Section: )mentioning
confidence: 99%
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