Results 31 to 40 of about 7,479,809 (288)
Moderate deviations and central limit theorem for positive diffusions
In this paper, we establish a central limit theorem and a moderate deviation principle for the positive diffusions, including the CEV and CIR models. The proof is based on the exponential approximations theorem and Burkholder-Davis-Gundy’s inequality.
Yumeng Li, Shuguang Zhang
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A Combinatorial Central Limit Theorem [PDF]
Let (Y n1,…,Y nn be a random vector which takes on the n! permutations of (1,…, n) with equal probabilities. Let c n(i,j), i,j = 1, …, n, be n real numbers. Sufficient conditions for the asymptotic normality of $$ S_n = \sum\limits_{i - 1}^n {c_n \left( {i,Y_{ni} } \right)} $$ are given (Theorem 3). For the special case c n(i,j) = a n(i)b n(j) a
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Convergence results for multivariate martingales [PDF]
We present a new version of the Central Limit Theorem for multivariate ...
L. Pratelli +3 more
core +1 more source
Stable limits for empirical processes on vapnik-cervonenk is classes of functions [PDF]
Alexander' s (1987) central limit theorem for empirical processes on Vapnik-Cervonenkis classes of functions is extended to the case with non-Gaussian stable limits.
Romo, Juan
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Almost Sure Central Limit Theorem for a Nonstationary Gaussian Sequence
Let be a standardized non-stationary Gaussian sequence, and let denote , . Under some additional condition, let the constants satisfy as for some and , for some , then, we have almost surely for any , where is the indicator function ...
Qing-pei Zang
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Tauberian Theorems and the Central Limit Theorem
We prove Tauberian theorems for random walks with positive drift obeying the central limit theorem. The results include (i) conclusions involving certain averages, relevant to number-theoretic densities and extending results of Diaconis and Stein; (ii) pointwise conclusions, including the classical Borel-Tauber theorem and extending results of Schmaal,
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dynoGP: Deep Gaussian Processes for Dynamic System Identification
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +2 more
wiley +1 more source
Multimodal Data‐Driven Microstructure Characterization
A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang +4 more
wiley +1 more source
Derivation of the Schrödinger equation III: the Central Limit Theorem [PDF]
In this paper, we show that the Central Limit Theorem is deeply ingrained in the mathematical and physical structure of Quantum Mechanics. We show, furthermore, that the Central Limit Theorem provides us with a clarification of the assumptions made by ...
Olavo L. da Silva Filho +1 more
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Tauberian theorems for limitation methods admitting a central limit theorem
If t is restricted to the integers, the method is a special case of the Sonnenschein methods (Zeller and Beekman [-16], p. 185) and as such admits a probabilistic interpretation. As general references to probability theory we mention Chung [-2], Parzen [-11], Feller [-5], Breiman [,1]. Let X 1, X 2 . . . .
Schmaal, A., Stam, A.J., Vries, T. de
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