scholarly journals Limiting distributions and almost sure limit theorems for the normalized maxima of complete and incomplete samples from Gaussian sequence

2009 ◽  
Vol 3 (0) ◽  
pp. 851-864 ◽  
Author(s):  
Zuoxiang Peng ◽  
Ping Wang ◽  
Saralees Nadarajah
Keyword(s):  
Limit Theorems ◽  
Gaussian Sequence ◽  
Limiting Distributions ◽  
Almost Sure Limit Theorems

scholarly journals Almost Sure Limit Theorems for Multivariate General Standard Normal Sequences and Applications

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Zhicheng Chen ◽  
Xinsheng Liu
Keyword(s):  
Central Limit ◽  
Limit Theorems ◽  
Almost Sure Convergence ◽  
Central Limit Theorems ◽  
Random Vectors ◽  
Standard Normal ◽  
Almost Sure Limit Theorems ◽  
General Standard

Under suitable conditions, the almost sure central limit theorems for the maximum of general standard normal sequences of random vectors are proved. The simulation of the almost sure convergence for the maximum is firstly performed, which helps to visually understand the theorems by applying to two new examples.

Almost sure limit theorems for the St. Petersburg game

1999 ◽  
Vol 45 (1) ◽  
pp. 23-30 ◽  
Author(s):  
István Berkes ◽  
Endre Csáki ◽  
Sándor Csörgő
Keyword(s):  
Limit Theorems ◽  
Almost Sure Limit Theorems

RANDOM WALKS WITH HEAVY TAILS AND LIMIT THEOREMS FOR BRANCHING PROCESSES WITH MIGRATION AND IMMIGRATION

2012 ◽  
Vol 12 (01) ◽  
pp. 1150007 ◽  
Author(s):  
YAQIN FENG ◽  
STANISLAV MOLCHANOV ◽  
JOSEPH WHITMEYER
Keyword(s):  
Random Walks ◽  
Limit Theorems ◽  
Heavy Tails ◽  
Stochastic Dynamics ◽  
Branching Processes ◽  
Spatial Dynamics ◽  
Limiting Distributions ◽  
Central Result

The central result of this paper is the existence of limiting distributions for two classes of critical homogeneous-in-space branching processes with heavy tails spatial dynamics in dimension d = 2. In dimension d ≥ 3, the same results are true without any special assumptions on the underlying (non-degenerated) stochastic dynamics.

Limit theorems for convex hulls of random sets

1993 ◽  
Vol 25 (02) ◽  
pp. 395-414 ◽  
Author(s):  
Ilya S. Molchanov
Keyword(s):  
Limit Theorems ◽  
Random Sets ◽  
Convex Hulls ◽  
Stable Sets ◽  
Limiting Distributions ◽  
Closed Set ◽  
Closed Sets ◽  
Random Closed Sets ◽  
Random Closed Set ◽  
Simple Limit

Let , be i.i.d. random closed sets in . Limit theorems for their normalized convex hulls conv () are proved. The limiting distributions correspond to C-stable random sets. The random closed set A is called C-stable if, for any , the sets anA and conv ( coincide in distribution for certain positive an, compact Kn , and independent copies A 1, …, An of A. The distributions of C-stable sets are characterized via corresponding containment functionals.

On records and related processes for sequences with trends

1999 ◽  
Vol 36 (3) ◽  
pp. 668-681 ◽  
Author(s):  
K. Borovkov
Keyword(s):  
Markov Chains ◽  
Limit Theorems ◽  
Convergence Rates ◽  
Rate Function ◽  
Regular Case ◽  
Limiting Distributions ◽  
Record Times ◽  
Ergodicity Theorem ◽  
Linear Trends

We study the records and related variables for sequences with linear trends. We discuss the properties of the asymptotic rate function and relationships between the distribution of the long-term maxima in the sequence and that of a particular observation, including two characterization type results. We also consider certain Markov chains related to the process of records and prove limit theorems for them, including the ergodicity theorem in the regular case (convergence rates are given under additional assumptions), and derive the limiting distributions for the inter-record times and increments of records.

Almost sure limit theorems of extremes of complete and incomplete samples of stationary sequences

Extremes ◽  
2009 ◽  
Vol 13 (4) ◽  
pp. 463-480 ◽  
Author(s):  
Zuoxiang Peng ◽  
Zhichao Weng ◽  
Saralees Nadarajah
Keyword(s):  
Limit Theorems ◽  
Stationary Sequences ◽  
Almost Sure Limit Theorems

scholarly journals Variations around Eagleson’s theorem on mixing limit theorems for dynamical systems

2019 ◽  
Vol 40 (12) ◽  
pp. 3368-3374 ◽  
Author(s):  
SÉBASTIEN GOUËZEL
Keyword(s):  
Dynamical Systems ◽  
Probability Measure ◽  
Limit Theorems ◽  
Absolutely Continuous ◽  
Almost Sure Limit Theorems

Eagleson’s theorem asserts that, given a probability-preserving map, if renormalized Birkhoff sums of a function converge in distribution, then they also converge with respect to any probability measure which is absolutely continuous with respect to the invariant one. We prove a version of this result for almost sure limit theorems, extending results of Korepanov. We also prove a version of this result, in mixing systems, when one imposes a conditioning both at time 0 and at time $n$.

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