Limiting distributions and almost sure limit theorems for the normalized maxima of complete and incomplete samples from Gaussian sequence
DOI10.1214/09-EJS443zbMath1326.62040OpenAlexW2015355243MaRDI QIDQ1952005
Ping Wang, Zuo Xiang Peng, Saralees Nadarajah
Publication date: 27 May 2013
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://projecteuclid.org/euclid.ejs/1250880018
limiting distributionmaximumstationary Gaussian sequencealmost sure limit theoremcomplete and incomplete samples
Asymptotic distribution theory in statistics (62E20) Extreme value theory; extremal stochastic processes (60G70) Strong limit theorems (60F15)
Cites Work
- On almost sure max-limit theorems
- On asymptotic distribution of maxima of complete and incomplete samples from stationary sequences
- An almost sure limit theorem for the maxima of strongly dependent Gaussian sequences
- Extremes and related properties of random sequences and processes
- Weak convergence for the maxima of stationary Gaussian processes using random normalization
- Limit distributions for the maxima of stationary Gaussian processes
- Almost sure limit theorems for the maximum of stationary Gaussian sequences
- A universal result in almost sure central limit theory.
- Almost sure convergence of extreme order statistics
- Almost sure limit theorems for a stationary normal sequence
- The almost sure central limit theorems in the joint version for the maxima and sums of certain stationary Gaussian sequences
- Almost sure max-limits for nonstationary Gaussian sequence
- Almost sure central limit theorem for partial sums and maxima
- Almost Sure Convergence in Extreme Value Theory
- Limit Theorems for the Maximum Term in Stationary Sequences
- Unnamed Item
This page was built for publication: Limiting distributions and almost sure limit theorems for the normalized maxima of complete and incomplete samples from Gaussian sequence
