Convolution equivalence and infinite divisibility
By Anthony G. Pakes. Journal of Applied Probability, 2004
169 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/05a95ac08e035dd9825f1baadfc251df9c3c2273
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Serguei Foss
- Thomas Opitz
- Dmitry Korshunov
- Toshiro Watanabe
- Kouji Yamamuro
- Qihe Tang
- Sebastian Engelke
- Jennifer L. Wadsworth
In Management Science and Operations Research
- Kouji Yamamuro
- Sebastian Engelke
- Thomas Opitz
- Jennifer L. Wadsworth
- Serguei Foss
- Dmitry Korshunov
- Toshiro Watanabe
- Qihe Tang
In Statistics and Probability
- Thomas Opitz
- Qihe Tang
- Kouji Yamamuro
- Serguei Foss
- Dmitry Korshunov
- Toshiro Watanabe
- Sebastian Engelke
- Jennifer L. Wadsworth
Citations in the corpus
- From light tails to heavy tails through multiplier (2008, background): “Recent studies on these classes are found in Pakes (2004), Shimura and Watanabe (2005), Tang (2006a), Su and Chen (2006), and Foss and Korshunov (2007), among others.”
- Extremal behavior of stochastic volatility models (2006, unknown): “1, or Pakes [32], Lemma 2.”
- Ratio of the Tail of an Infinitely Divisible Distribution on the Line to that of its Lévy Measure (2010, background): “1 of [24], and conclusively to Theorem 1.”
- Lower limits and equivalences for convolution tails (2005, background): “MR0929511 [9] Pakes, A. G. (2004).”
- Extremal dependence of random scale constructions (2018, background): “Lemma 8 (Breiman’s lemma, see Breiman (1965), Cline and Samorodnitsky (1994), and Pakes (2004), Lemma 2.1) Suppose X ∼ F , Y ∼ G are independent random variables.”
- Convolution equivalence and distributions of random sums (2008, methodology): “Theorem 5.1 of Pakes [ 23 ] asserted a two-sided extension of Theorem 2.13 of [4].”