A comparison of methods for selecting values of simulation input variables
ESAIM: Probability and Statistics, Tome 19 (2015), pp. 135-147.

Refined descriptive sampling (RDS) is a method of sampling that can be used to produce input values for estimation of expectation of functions of output variables. This paper gives a generalization of RDS method for K input variables. An estimator of RDS is defined and shown to be unbiased and efficient compared to simple random sampling with respect to variance criterion for a class of estimators. The efficiency of RDS algorithm is discussed at the end of the paper.

Reçu le :
DOI : 10.1051/ps/2014020
Classification : 62D05, 65C05, 62H12, 37M05
Mots-clés : Sampling, variance, estimation, simulation
Ourbih-Tari, Megdouda 1 ; Guebli, Sofia 1

1 Laboratoire de Mathématiques Appliquées, Faculté des Sciences Exactes, Université de Bejaia, 06000 Bejaia, Algérie
@article{PS_2015__19__135_0,
     author = {Ourbih-Tari, Megdouda and Guebli, Sofia},
     title = {A comparison of methods for selecting values of simulation input variables},
     journal = {ESAIM: Probability and Statistics},
     pages = {135--147},
     publisher = {EDP-Sciences},
     volume = {19},
     year = {2015},
     doi = {10.1051/ps/2014020},
     mrnumber = {3386367},
     zbl = {1351.62027},
     language = {en},
     url = {http://archive.numdam.org/articles/10.1051/ps/2014020/}
}
TY  - JOUR
AU  - Ourbih-Tari, Megdouda
AU  - Guebli, Sofia
TI  - A comparison of methods for selecting values of simulation input variables
JO  - ESAIM: Probability and Statistics
PY  - 2015
SP  - 135
EP  - 147
VL  - 19
PB  - EDP-Sciences
UR  - http://archive.numdam.org/articles/10.1051/ps/2014020/
DO  - 10.1051/ps/2014020
LA  - en
ID  - PS_2015__19__135_0
ER  - 
%0 Journal Article
%A Ourbih-Tari, Megdouda
%A Guebli, Sofia
%T A comparison of methods for selecting values of simulation input variables
%J ESAIM: Probability and Statistics
%D 2015
%P 135-147
%V 19
%I EDP-Sciences
%U http://archive.numdam.org/articles/10.1051/ps/2014020/
%R 10.1051/ps/2014020
%G en
%F PS_2015__19__135_0
Ourbih-Tari, Megdouda; Guebli, Sofia. A comparison of methods for selecting values of simulation input variables. ESAIM: Probability and Statistics, Tome 19 (2015), pp. 135-147. doi : 10.1051/ps/2014020. http://archive.numdam.org/articles/10.1051/ps/2014020/

I.T. Dimov, Monte Carlo methods for applied scientists. World Scientific Publishing Co. Pte, Ltd Singapore (2008). | MR | Zbl

G.S. Fishman, Monte-Carlo Concepts Algorithms and Applications. Springer-Verlag, Berlin (1997). | MR | Zbl

M. Grigoriu, A spectral-based Monte Carlo algorithm for generating samples of nonstationary Gaussian processes. Monte Carlo Methods Appl. 16 (2010) 143–165. | MR | Zbl

A. Keller, S. Heinrich and H. Niederreiter, Monte Carlo and Quasi Monte Carlo Methods 2008. Springer (2008). | MR | Zbl

E.L. Lehmann, Some concepts of dependence. Ann. Math. Statist. 35 (1966) 1137–1153. | MR | Zbl

W.L. Loh, On Latin hypercube sampling. Ann. Statist. 24 (1996) 2058–2080. | MR | Zbl

M.D. Mackay, R.J. Beckman and W.J. Conover, A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21 (1979) 239-245. | MR | Zbl

H. Niederreiter, Random number generation and quasi monte Carlo methods. Society for Industrial and Applied Mathematics, Philadelphia (1992). | MR | Zbl

M. Ourbih-Tari and A. Aloui, Sampling methods and parallelism into Monte Carlo simulation. J. Statist. Adv. Theory Appl. 1 (2009) 169–192. | Zbl

M. Ourbih-Tari, A. Aloui and A. Alioui, A software component which generates regular numbers from refined descriptive sampling, in Proc. of the European Simulation Modelling conference, edited by Marwan Al-Akaidi. Leicester United Kingdom (2009) 23–25.

M. Pidd, Computer simulation in management science, 5 edn. John Wiley and Sons (2004).

J.S. Ramberg and B.W. Schmeiser, An Approximate Method for generating Symmetric Random Variables. Communications of the ACM 15 (1972) 987–990. | Zbl

E. Saliby, Descriptive Sampling A better approach to Monte Carlo simulation. J. Oper. Res. Soc. 41 (1990) 1133–1142.

T.L. Schmitz and H.S. Kim, Monte Carlo evaluation of periodic error uncertainty. Precision Engrg. 31 (2007) 251-259.

I.M. Sobol, A primer for the Monte Carlo method. CRS Press (1994). | MR | Zbl

M. Tari and A. Dahmani, Flowshop simulator using different sampling methods. Oper. Res. Inter. J. 5 (2005) 261–272.

M. Tari and A. Dahmani, The three phase discrete event simulation using some sampling methods. Int. J. Appl. Math. Statist. 3 (2005) 37–48. | MR | Zbl

M. Tari and A. Dahmani, Refined Descriptive Sampling A better approach to Monte Carlo simulation. Simul. Model. Pract. Theory 14 (2006) 143–160.

Cité par Sources :