Results 261 to 270 of about 49,053 (278)
Some of the next articles are maybe not open access.
On the asymptotic approximation of inverse moment under sub-linear expectations
Journal of Mathematical Analysis and Applications, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wu, Yi, Wang, Xuejun, Zhang, Lixin
openaire +1 more source
Communications in Mathematics and Statistics, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wu, Yi +3 more
openaire +1 more source
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wu, Yi +3 more
openaire +1 more source
Strong Limit Theorems for Weighted Sums under the Sub-linear Expectations
Acta Mathematicae Applicatae Sinica, English SerieszbMATH Open Web Interface contents unavailable due to conflicting licenses.
Feng, Feng-xiang +3 more
openaire +1 more source
Bulletin of the Malaysian Mathematical Sciences Society, 2023
Given a measurable space \((\Omega, \mathcal{F})\), consider a collection \(\mathcal{P}\) of probability measures on \((\Omega, \mathcal{F})\). Now, define the following upper and lower probabilities \[ \mathbb{P}(A) = \sup_{P \in \mathcal{P}} P(A), \text{ and } \mathsf{P}(A) = \inf_{P \in \mathcal{P}} P(A), \tag{1} \] for all \(A\in \mathcal{F ...
Mengmei Xi, Fei Zhang, Xuejun Wang
openaire +1 more source
Given a measurable space \((\Omega, \mathcal{F})\), consider a collection \(\mathcal{P}\) of probability measures on \((\Omega, \mathcal{F})\). Now, define the following upper and lower probabilities \[ \mathbb{P}(A) = \sup_{P \in \mathcal{P}} P(A), \text{ and } \mathsf{P}(A) = \inf_{P \in \mathcal{P}} P(A), \tag{1} \] for all \(A\in \mathcal{F ...
Mengmei Xi, Fei Zhang, Xuejun Wang
openaire +1 more source
An elementary proof of Peng’s central limit theorem under sub-linear expectations
International Journal of Financial Engineering, 2020Motivated by studies in mathematical finance, S. Peng established a central limit theorem (CLT) under sub-linear expectations by using partial differential equations (PDEs). In this paper, we provide a novel proof of Peng’s CLT in the frame of sub-linear expectation.
Zengjing Chen, Ziwu Zhang
openaire +1 more source
Weak and strong laws of large numbers for sub-linear expectation
Communications in Statistics - Theory and Methods, 2019AbstractIn this paper, we derive a new form of weak laws of large numbers for sub-linear expectation and establish the equivalence relation among this new form and the other two forms of weak laws ...
openaire +1 more source
The laws of large numbers for Pareto-type random variables under sub-linear expectation
Frontiers of Mathematics, 2022Very exciting basic definitions of probability calculation theory are expanded: the relevant conclusions in the traditional probability space are extended to the sub-linear expectation space. From a new aspect, one of the most beautiful theorems of probability calculation, the weak law of large numbers and strong law of large numbers of the weighted ...
Chen, Binxia, Wu, Qunying
openaire +2 more sources
Complete convergence theorem for negatively dependent random variables under sub-linear expectations
Communications in Statistics - Theory and Methods, 2020Under the condition that the Choquet integral exists, we study the complete convergence theorem for negatively dependent random variables under sub-linear expectation space.
Binxia Chen, Qunying Wu
openaire +1 more source
Cross-Language High Similarity Search: Why No Sub-linear Time Bound Can Be Expected
2010This paper contributes to an important variant of cross-language information retrieval, called cross-language high similarity search. Given a collection D of documents and a query q in a language different from the language of D, the task is to retrieve highly similar documents with respect to q.
Maik Anderka +2 more
openaire +1 more source

