Towards a universal self-normalized moderate deviation
From MaRDI portal
Publication:3518201
DOI10.1090/S0002-9947-08-04402-4zbMath1143.60022OpenAlexW2083022876MaRDI QIDQ3518201
Bing-Yi Jing, Qui-Man Shao, Wang Zhou
Publication date: 7 August 2008
Published in: Transactions of the American Mathematical Society (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1090/s0002-9947-08-04402-4
Sums of independent random variables; random walks (60G50) Strong limit theorems (60F15) Large deviations (60F10)
Related Items (9)
Self-normalization: taming a wild population in a heavy-tailed world ⋮ A nonclassical LIL for geometrically weighted series in Banach space ⋮ Strassen-type law of the iterated logarithm for self-normalized sums ⋮ A strong approximation of self-normalized sums ⋮ Strassen-type law of the iterated logarithm for self-normalized increments of sums ⋮ Tests alternative to higher criticism for high-dimensional means under sparsity and column-wise dependence ⋮ Matrix Factor Analysis: From Least Squares to Iterative Projection ⋮ Laws of the iterated logarithm for self-normalised Lévy processes at zero ⋮ Weak Convergence of Self-normalized Partial Sums Processes
Cites Work
- Unnamed Item
- Self-normalized laws of the iterated logarithm
- On the LIL for self-normalized sums of IID random variables
- Self-normalized large deviations
- When is the Student \(t\)-statistic asymptotically standard normal?
- Darling-Erdős theorem for self-normalized sums
- Donsker's theorem for self-normalized partial sums processes
- Self-normalized Cramér-type large deviations for independent random variables.
- Tightness of the Student \(t\)-statistic
- Limit distributions of Studentized means.
- The Berry-Esseen bound for Student's statistic
- Limit distributions of self-normalized sums
- Saddlepoint approximation for Student's \(t\)-statistic with no moment conditions
- Some universal results on the behavior of increments of partial sums
- Uniform Central Limit Theorems
This page was built for publication: Towards a universal self-normalized moderate deviation
