Zeitschrift fur Wahrscheinlichkeitstheorie und Verwandte Gebiete.
By L. Schmetterer. 1963
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Citations in the corpus
- A Kernel Independence Test for Random Processes (2014, methodology): “By the CLT for U -statistics from Denker & Keller (1983), Theorem 1(c), we obtain lim n→∞ 1 n(n− 1)(n− 2) Sn P = 6Eh(Z∗1 , Z∗1 , Z∗2 , Z∗3 ).”
- Posterior Computation with the Gibbs Zig-Zag Sampler (2020, background): “By Bhattacharya (1982) and the boundedness of the inverse of the generator, a central limit theorem is obtained as follows.”
- Convexity/concavity of renyi entropy and α-mutual information (2015, background): “This notion of mutual information was introduced by Sibson [10] in the discrete case, as the information radius of order α [11].”
- Flexible Option Learning (2021, methodology): “We verify this in Figure 1c where we plot the information radius [Sibson, 1969] between options as a measure of diversity.”
- Nonparametric Kernel Estimation for Semiparametric Models (1995, methodology): “It was introduced by Ibragimov (1962), and results utilizing it were developed by Billingsley (1968, p. 182) and McLeish (1975a, 1975b, 1977), among others.”
- GARCH Models (2010, unknown): “It can be shown that the assumptions on the distribution of ηt imply that inequality (2.39) is strict, that is, γ < log ρ(A) (see Kesten and Spitzer, 1984, Theorem 2; Bougerol and Picard, 1992b, Corollary 2.2).”
- Riemannian Score-Based Generative Modeling (2022, methodology): “Hence, we use Geodesic Random Walks which converge to the diffusion of interest in the limit of small stepsizes (Jørgensen, 1975).”
- Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons (2023, background): “And the last inequality is due to the Bretagnolle–Huber inequality (Bretagnolle and Huber, 1979).”
- Applied Nonparametric Regression: The kernel method (1990, unknown): “The special case of Si = Xi has been investigated by Cheng and Lin (1981). A notion of an asymptotic equivalence of the weight sequences {W (2) hi } and {W (3) hi } is deferred to the Exercises.”
- A weak solution theory for stochastic Volterra equations of convolution type (2019, background): “…= ζ. Standard representation theorems for semimartingales allow us to express Z as a stochastic integral with respect to time, Brownian motion, and a compensated Poisson random measure; see Jacod and Protter (2011, Theorem 2.1.2) and El Karoui and Lepeltier (1977); Lepeltier and Marchal (1976).”