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Minimum mean-squared-error estimators for simulation experiments

Authors:

James H. Donnelly,

Robert E. ShannonAuthors Info & Claims

Communications of the ACM, Volume 24, Issue 4

Pages 253 - 259

https://doi.org/10.1145/358598.358637

Published: 01 April 1981 Publication History

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Cited By

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Alsadi NGadsden SYawney J(2023)Intelligent estimation: A review of theory, applications, and recent advancesDigital Signal Processing10.1016/j.dsp.2023.103966135(103966)Online publication date: Apr-2023
https://doi.org/10.1016/j.dsp.2023.103966
Avzayesh MAbdel-Hafez MAlShabi MGadsden S(2021)The smooth variable structure filter: A comprehensive reviewDigital Signal Processing10.1016/j.dsp.2020.102912110(102912)Online publication date: Mar-2021
https://doi.org/10.1016/j.dsp.2020.102912
Samore MShen SGreene TStoddard GSauer BShinogle JNebeker JHarbarth S(2007)A Simulation-Based Evaluation of Methods to Estimate the Impact of an Adverse Event on Hospital Length of StayMedical Care10.1097/MLR.0b013e318074ce8a45:Suppl 2(S108-S115)Online publication date: Oct-2007
https://doi.org/10.1097/MLR.0b013e318074ce8a
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Index Terms

Minimum mean-squared-error estimators for simulation experiments
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
2. Mathematics of computing
  1. Probability and statistics

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Published In

Communications of the ACM Volume 24, Issue 4

Special issue on simulation modeling and statistical computing

April 1981

102 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/358598

Editors:
Robert L. Ashenhurst
The Univ. of Chicago, Chicago, IL
,
Nabil R. Adam
Rutgers Univ., Piscataway, NJ

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 April 1981

Published in CACM Volume 24, Issue 4

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Alsadi NGadsden SYawney J(2023)Intelligent estimation: A review of theory, applications, and recent advancesDigital Signal Processing10.1016/j.dsp.2023.103966135(103966)Online publication date: Apr-2023
https://doi.org/10.1016/j.dsp.2023.103966
Avzayesh MAbdel-Hafez MAlShabi MGadsden S(2021)The smooth variable structure filter: A comprehensive reviewDigital Signal Processing10.1016/j.dsp.2020.102912110(102912)Online publication date: Mar-2021
https://doi.org/10.1016/j.dsp.2020.102912
Samore MShen SGreene TStoddard GSauer BShinogle JNebeker JHarbarth S(2007)A Simulation-Based Evaluation of Methods to Estimate the Impact of an Adverse Event on Hospital Length of StayMedical Care10.1097/MLR.0b013e318074ce8a45:Suppl 2(S108-S115)Online publication date: Oct-2007
https://doi.org/10.1097/MLR.0b013e318074ce8a
Pawlikowski K(1990)Steady-state simulation of queueing processes: survey of problems and solutionsACM Computing Surveys10.1145/78919.7892122:2(123-170)Online publication date: 1-Jun-1990
https://dl.acm.org/doi/10.1145/78919.78921
Griffith DGriffith D(1988)Simulation Experimentation in Spatial AnalysisAdvanced Spatial Statistics10.1007/978-94-009-2758-2_9(225-260)Online publication date: 1988
https://doi.org/10.1007/978-94-009-2758-2_9
Law A(1983)Feature Article-Statistical Analysis of Simulation Output DataOperations Research10.1287/opre.31.6.98331:6(983-1029)Online publication date: 1-Dec-1983
https://dl.acm.org/doi/10.1287/opre.31.6.983

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Optimal Mean-Squared-Error Batch Sizes

Minimum Mean Square Error Estimators for the Exponential SSALT Model

Non-parametric bootstrap mean squared error estimation for M-quantile estimators of small area averages, quantiles and poverty indicators

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