Strong Law of Large Numbers Under a General Moment Condition
Dublin Core | PKP Metadata Items | Metadata for this Document | |
1. | Title | Title of document | Strong Law of Large Numbers Under a General Moment Condition |
2. | Creator | Author's name, affiliation, country | Sergei Chobanyan; Georgian Academy of Sciences, Georgia |
2. | Creator | Author's name, affiliation, country | Shlomo Levental; Michigan State University, USA |
2. | Creator | Author's name, affiliation, country | Habib Salehi; Michigan State University, USA |
3. | Subject | Discipline(s) | |
3. | Subject | Keyword(s) | |
4. | Description | Abstract | We use our maximum inequality for $p$-th order random variables ($p>1$) to prove a strong law of large numbers (SLLN) for sequences of $p$-th order random variables. In particular, in the case $p=2$ our result shows that $\sum f(k)/k < \infty$ is a sufficient condition for SLLN for $f$-quasi-stationary sequences. It was known that the above condition, under the additional assumption of monotonicity of $f$, implies SLLN (Erdos (1949), Gal and Koksma (1950), Gaposhkin (1977), Moricz (1977)). Besides getting rid of the monotonicity condition, the inequality enables us to extend thegeneral result to $p$-th order random variables, as well as to the case of Banach-space-valued random variables. |
5. | Publisher | Organizing agency, location | |
6. | Contributor | Sponsor(s) | |
7. | Date | (YYYY-MM-DD) | 2005-10-03 |
8. | Type | Status & genre | Peer-reviewed Article |
8. | Type | Type | |
9. | Format | File format | |
10. | Identifier | Uniform Resource Identifier | http://ecp.ejpecp.org/article/view/1156 |
10. | Identifier | Digital Object Identifier | 10.1214/ECP.v10-1156 |
11. | Source | Journal/conference title; vol., no. (year) | Electronic Communications in Probability; Vol 10 |
12. | Language | English=en | |
14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
15. | Rights | Copyright and permissions | The Electronic Journal of Probability applies the Creative Commons Attribution License (CCAL) to all articles we publish in this journal. Under the CCAL, authors retain ownership of the copyright for their article, but authors allow anyone to download, reuse, reprint, modify, distribute, and/or copy articles published in EJP, so long as the original authors and source are credited. This broad license was developed to facilitate open access to, and free use of, original works of all types. Applying this standard license to your work will ensure your right to make your work freely and openly available. Summary of the Creative Commons Attribution License You are free
|