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Titlebook: Concentration and Gaussian Approximation for Randomized Sums; Sergey Bobkov,Gennadiy Chistyakov,Friedrich G?tze Book 2023 The Editor(s) (i

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樓主: Lactase
21#
發(fā)表于 2025-3-25 04:59:01 | 只看該作者
22#
發(fā)表于 2025-3-25 08:37:22 | 只看該作者
https://doi.org/10.1007/978-1-4939-2602-2The aim is in particular to quantify the asymptotic normality of these distributions and to include dimensional refinements of such approximation in analogy with Edgeworth expansions (which however we consider up to order 2).
23#
發(fā)表于 2025-3-25 13:29:50 | 只看該作者
24#
發(fā)表于 2025-3-25 17:58:17 | 只看該作者
Time-Series Prediction and ApplicationsIn order to study deviations of the distribution functions . from the typical distribution . by means of the Kolmogorov distance, Berry–Esseen-type inequalities, which we discussed in Chapter 3, will be used. To this end we need to focus first on the behavior of characteristic functions of ..
25#
發(fā)表于 2025-3-25 20:04:11 | 只看該作者
Amit Konar,Diptendu BhattacharyaIn order to deal with the main Problem 12.1.2, we start with the Kantorovich distance for bounding possible fluctuations of . around . on average.
26#
發(fā)表于 2025-3-26 01:17:44 | 只看該作者
Moments and Correlation ConditionsThis definition is frequently used in Convex Geometry, especially for random vectors which are uniformly distributed over a convex body (in which case the body is called isotropic, cf. [144]).
27#
發(fā)表于 2025-3-26 05:05:27 | 只看該作者
Standard Analytic ConditionsIn some problems/Sobolev-type inequalities, it makes sense to slightly modify the notion of the generalized modulus of gradient.
28#
發(fā)表于 2025-3-26 10:05:53 | 只看該作者
29#
發(fā)表于 2025-3-26 14:46:41 | 只看該作者
Sobolev-type InequalitiesAccording to the general equation (5.4), and since the geodesic and Euclidean distances are infinitesimally equivalent, the second order modulus of the gradient for functions . on the unit sphere is defined by
30#
發(fā)表于 2025-3-26 18:49:21 | 只看該作者
Linear Functionals on the SphereThe aim is in particular to quantify the asymptotic normality of these distributions and to include dimensional refinements of such approximation in analogy with Edgeworth expansions (which however we consider up to order 2).
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