On the Efficiency of Monte Carlo Methods Using Steady State Ranked Simulated Samples

Mohammad Fraiwan Al-Saleh; Hani Samawi

doi:10.1080/03610910008813647

On the Efficiency of Monte Carlo Methods Using Steady State Ranked Simulated Samples

Mohammad Fraiwan Al-Saleh, Hani Samawi

Biostatistics, Epidemiology & Environmental Health Sciences

Yarmouk University

Research output: Contribution to journal › Article › peer-review

20 Scopus citations

Abstract

Samawi (1999) showed that the efficiency of Monte Carlo methods of integrals estimation can be substantially improved by using ranked simulated samples (RSIS) in place of uniform simulated samples (USIS). However, in this paper it is shown that substantial improvement of efficiency can be achieved further by using the steady state ranked simulated sample (SRSIS). It appears that the modified Monte Carlo methods using SRSIS provide unbiased and more efficient estimators for the integrals. Some theoretical properties of SRSIS are given. A simulation study is conducted to compare the performance of the methods using SRSIS with respect to USIS, for some examples.

Original language	American English
Journal	Communication in Statistics - Simulation and Computation
Volume	29
DOIs	https://doi.org/10.1080/03610910008813647
State	Published - 2000

Disciplines

Biostatistics

Keywords

antithetic sampling
control variate
crude sampling
importance sampling
order statistics
ranked simulated sample
steady state ranked simulated sample
uniform simulated sample

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title = "On the Efficiency of Monte Carlo Methods Using Steady State Ranked Simulated Samples",

abstract = " Samawi (1999) showed that the efficiency of Monte Carlo methods of integrals estimation can be substantially improved by using ranked simulated samples (RSIS) in place of uniform simulated samples (USIS). However, in this paper it is shown that substantial improvement of efficiency can be achieved further by using the steady state ranked simulated sample (SRSIS). It appears that the modified Monte Carlo methods using SRSIS provide unbiased and more efficient estimators for the integrals. Some theoretical properties of SRSIS are given. A simulation study is conducted to compare the performance of the methods using SRSIS with respect to USIS, for some examples.",

keywords = "antithetic sampling, control variate, crude sampling, importance sampling, order statistics, ranked simulated sample, steady state ranked simulated sample, uniform simulated sample",

author = "Al-Saleh, {Mohammad Fraiwan} and Hani Samawi",

note = "Samawi (1999) showed that the efficiency of Monte Carlo methods of integrals estimation can be substantially improved by using ranked simulated samples (RSIS) in place of uniform simulated samples (USIS). However, in this paper it is shown that substantial improvement of efficiency can be achieved further by using the steady state ranked simulated sample (SRSIS).",

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doi = "10.1080/03610910008813647",

language = "American English",

volume = "29",

journal = "Communication in Statistics - Simulation and Computation",

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T1 - On the Efficiency of Monte Carlo Methods Using Steady State Ranked Simulated Samples

AU - Al-Saleh, Mohammad Fraiwan

AU - Samawi, Hani

N1 - Samawi (1999) showed that the efficiency of Monte Carlo methods of integrals estimation can be substantially improved by using ranked simulated samples (RSIS) in place of uniform simulated samples (USIS). However, in this paper it is shown that substantial improvement of efficiency can be achieved further by using the steady state ranked simulated sample (SRSIS).

PY - 2000

Y1 - 2000

N2 - Samawi (1999) showed that the efficiency of Monte Carlo methods of integrals estimation can be substantially improved by using ranked simulated samples (RSIS) in place of uniform simulated samples (USIS). However, in this paper it is shown that substantial improvement of efficiency can be achieved further by using the steady state ranked simulated sample (SRSIS). It appears that the modified Monte Carlo methods using SRSIS provide unbiased and more efficient estimators for the integrals. Some theoretical properties of SRSIS are given. A simulation study is conducted to compare the performance of the methods using SRSIS with respect to USIS, for some examples.

AB - Samawi (1999) showed that the efficiency of Monte Carlo methods of integrals estimation can be substantially improved by using ranked simulated samples (RSIS) in place of uniform simulated samples (USIS). However, in this paper it is shown that substantial improvement of efficiency can be achieved further by using the steady state ranked simulated sample (SRSIS). It appears that the modified Monte Carlo methods using SRSIS provide unbiased and more efficient estimators for the integrals. Some theoretical properties of SRSIS are given. A simulation study is conducted to compare the performance of the methods using SRSIS with respect to USIS, for some examples.

KW - antithetic sampling

KW - control variate

KW - crude sampling

KW - importance sampling

KW - order statistics

KW - ranked simulated sample

KW - steady state ranked simulated sample

KW - uniform simulated sample

UR - https://doi.org/10.1080/03610910008813647

U2 - 10.1080/03610910008813647

DO - 10.1080/03610910008813647

M3 - Article

VL - 29

JO - Communication in Statistics - Simulation and Computation

JF - Communication in Statistics - Simulation and Computation

ER -

On the Efficiency of Monte Carlo Methods Using Steady State Ranked Simulated Samples

Abstract

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Keywords

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