CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Applying Stochastic Approximation Method with Delayed Observations in Exponential Distribution Case

by R. A. Atwa
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 109 - Number 17
Year of Publication: 2015
Authors: R. A. Atwa
10.5120/19427-0856

R. A. Atwa . Applying Stochastic Approximation Method with Delayed Observations in Exponential Distribution Case. International Journal of Computer Applications. 109, 17 ( January 2015), 35-38. DOI=10.5120/19427-0856

@article{ 10.5120/19427-0856,
author = { R. A. Atwa },
title = { Applying Stochastic Approximation Method with Delayed Observations in Exponential Distribution Case },
journal = { International Journal of Computer Applications },
issue_date = { January 2015 },
volume = { 109 },
number = { 17 },
month = { January },
year = { 2015 },
issn = { 0975-8887 },
pages = { 35-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume109/number17/19427-0856/ },
doi = { 10.5120/19427-0856 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:45:06.700493+05:30
%A R. A. Atwa
%T Applying Stochastic Approximation Method with Delayed Observations in Exponential Distribution Case
%J International Journal of Computer Applications
%@ 0975-8887
%V 109
%N 17
%P 35-38
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The main purpose of this work is investigated a loss system, which can serve as a model of modified Robbins-Monro stochastic approximation in the presence of delayed observations. Here we confine ourselves to the case of exponential distribution The results achieved for the loss system enable to conclude about the efficiency of the procedure and to give a hint for the choice of the number of servers in the modified loss system.

References
  1. Albert, A. E. and Gamder, L. A. 1967 Stochastic Approximation and Nonlinear Regression. MIT press, Cambridge.
  2. Cheung, Y. and Elkind,M. S. V. 2010. Stochastic approximation with virtual observations for dose finding on discrete levels. Biometrika, 97, 109-121.
  3. Chung,K. L. 1954. On A Stochastic Approximation Method. Ann. Math. Statist. 25, 463-483.
  4. Dimous, S. and Fakinos, D. 2010. Equilibrium results for the M/G/K group-arrival loss system. Socieded de Estadistica e Investigacion Operativa.
  5. Doob, J. L. 1953. Stochastic Processes. Wiley New York; Chapman and Hall, London.
  6. Dupa?, V. and Herkenrath, U. 1985. Stochastic Approximation with delayed obser-vations. Biometrika, 72, 683-685.
  7. Fabian, V. 1971. Stochastic Approximation. In Optimizing Methods in Statistics. (J. S. Rustagi, ed. ), Academic press, New York. 439-470.
  8. Harold, J. Kushner, G. and George Yin. 1997. Stochastic Approximation and Recursive Algorithm and applications. Applications of Mathematics, 35.
  9. Joseph, VR. Tian, Y. and Wu, CFJ. 2007. Adaptive Designs for Stochastic Root-Finding. Statistica Sinica, 17, 1549-1565.
  10. Kesten, H. 1958. Accelerated Stochastic Approximation. Ann. Math. Statist. , 29, 41-59.
  11. Kiefer, J. and Wolfowitz, J. 1952. Stochastic Approximation of the Maximum of a regression function, Ann. Math. Statis.
  12. Konev, V. and Pergamenshchikov, S. 2003. Sequential estimation in Stochastic Approximation with Autoregressive errors in observations. Sequential Analysis, 22, 1-29.
  13. Lai, T. 2003. Stochastic approximation. The Annals of Statistics, 31, 391-406.
  14. M. A. Mahmoud and A. A. Rasha, Stochastic Approximation with Compound Delayed Observations. Mathematical & Computational Applications, 10, 283-289.
  15. Mahmoud, M. A. and Rasha A. A. Stochastic Approximation and Compound Delayed Observations with Independent Random Time Delay Distribution. Arabian Journal of Science and Engineering,36, 1549-1558, 2011.
  16. Mokaddis , G. S. Metwally S. A. and Ahmed, A. M. 2000. The General Three-Server Queuing Loss System: Discrete-Time Analysis. Information and Management Sciences, 11, 81-95.
  17. Mukerjee, H. G. 1981. A Stochastic Approximation by Observation on Discrete Lattice Using Isotonic Regression. Ann. Math. Statist. 9, 1020- 1025.
  18. Robbins, H. and Monro, S. 1951. A stochastic approximation method. Ann. Math. Statist, 22, 400-407.
  19. Wasan, M. T. 1969. Stochastic Approximation. Cambridge Univ. Press, New Yoork.
  20. Xu, Z. and Dai, Y. 2008. A Stochastic Approximation Frame Algorithm with Adaptive Directions. Numerical Mathematics: Theory, Methods and Applications, 1, 460-474.
  21. Zhu, Y. and Yin, G. 1994. Stochastic approximation in real time: a pipe line approach. J. Comput. Math. , 12 , 21-30.
Index Terms

Computer Science
Information Sciences

Keywords

Stochastic approximation efficiency of the procedure

Powered by PhDFocusTM