Concentration inequality and the weak law of large numbers for the sum of partly negatively dependent \(\varphi\)-subgaussian random variables
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Publication:6152239
DOI10.1016/j.spl.2023.109979OpenAlexW4388923083MaRDI QIDQ6152239
Publication date: 13 February 2024
Published in: Statistics \& Probability Letters (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.spl.2023.109979
Cites Work
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- A note on the almost sure convergence of sums of negatively dependent random variables
- Moment inequalities and weak convergence for negatively associated sequences
- Concentration of dynamic risk measures in a Brownian filtration
- Convergence of series of dependent \(\varphi \)-sub-Gaussian random variables
- Non-asymptotic theory of random matrices: extreme singular values
- Probability Inequalities for the Sum of Independent Random Variables
- High-Dimensional Probability
- Large deviations for sums of partly dependent random variables
- Using Black-Box Compression Algorithms for Phase Retrieval
- Liquidity, Risk Measures, and Concentration of Measure
- Concentration Inequalities for Sums and Martingales
- Probability Inequalities for Sums of Bounded Random Variables
- Advanced Lectures on Machine Learning
- Some applications of concentration inequalities to statistics
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