Measuring what's missing: Practical estimates of coverage for stochastic simulations

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Standard

Measuring what's missing : Practical estimates of coverage for stochastic simulations. / Jones, Edward Samuel.

In: Journal of Statistical Computation and Simulation, Vol. 86, No. 9, 2016, p. 1660-1672.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Jones, ES 2016, 'Measuring what's missing: Practical estimates of coverage for stochastic simulations', Journal of Statistical Computation and Simulation, vol. 86, no. 9, pp. 1660-1672. https://doi.org/10.1080/00949655.2015.1077839

APA

Jones, E. S. (2016). Measuring what's missing: Practical estimates of coverage for stochastic simulations. Journal of Statistical Computation and Simulation, 86(9), 1660-1672. https://doi.org/10.1080/00949655.2015.1077839

Vancouver

Jones ES. Measuring what's missing: Practical estimates of coverage for stochastic simulations. Journal of Statistical Computation and Simulation. 2016;86(9):1660-1672. https://doi.org/10.1080/00949655.2015.1077839

Author

Jones, Edward Samuel. / Measuring what's missing : Practical estimates of coverage for stochastic simulations. In: Journal of Statistical Computation and Simulation. 2016 ; Vol. 86, No. 9. pp. 1660-1672.

Bibtex

@article{2db476c7b3dc4be69f095ccf587fda29,
title = "Measuring what's missing: Practical estimates of coverage for stochastic simulations",
abstract = "Stochastic sensitivity analyses rarely measure the extent to which realized simulations cover the search space. Rather, simulation lengths are typically chosen according to expert judgement. In response, this paper recommends a novel application of Good-Turing estimators of missing distributional mass. Using the UNDP's Human Development Index, the empirical performance of such coverage metrics are compared to alternative measures of convergence. The former are advantageous -- they provide probabilistic estimates of simulation coverage and permit calculation of strict bounds on estimates of pairwise dominance (for all possible weight vectors, how often country X dominates country Y).",
keywords = "Faculty of Social Sciences, sensitivity analysis, uncertainty analysis, Monte Carlo, simulation coverage, HDI",
author = "Jones, {Edward Samuel}",
year = "2016",
doi = "10.1080/00949655.2015.1077839",
language = "English",
volume = "86",
pages = "1660--1672",
journal = "Journal of Statistical Computation and Simulation",
issn = "0094-9655",
publisher = "Taylor & Francis",
number = "9",

}

RIS

TY - JOUR

T1 - Measuring what's missing

T2 - Practical estimates of coverage for stochastic simulations

AU - Jones, Edward Samuel

PY - 2016

Y1 - 2016

N2 - Stochastic sensitivity analyses rarely measure the extent to which realized simulations cover the search space. Rather, simulation lengths are typically chosen according to expert judgement. In response, this paper recommends a novel application of Good-Turing estimators of missing distributional mass. Using the UNDP's Human Development Index, the empirical performance of such coverage metrics are compared to alternative measures of convergence. The former are advantageous -- they provide probabilistic estimates of simulation coverage and permit calculation of strict bounds on estimates of pairwise dominance (for all possible weight vectors, how often country X dominates country Y).

AB - Stochastic sensitivity analyses rarely measure the extent to which realized simulations cover the search space. Rather, simulation lengths are typically chosen according to expert judgement. In response, this paper recommends a novel application of Good-Turing estimators of missing distributional mass. Using the UNDP's Human Development Index, the empirical performance of such coverage metrics are compared to alternative measures of convergence. The former are advantageous -- they provide probabilistic estimates of simulation coverage and permit calculation of strict bounds on estimates of pairwise dominance (for all possible weight vectors, how often country X dominates country Y).

KW - Faculty of Social Sciences

KW - sensitivity analysis

KW - uncertainty analysis

KW - Monte Carlo

KW - simulation coverage

KW - HDI

U2 - 10.1080/00949655.2015.1077839

DO - 10.1080/00949655.2015.1077839

M3 - Journal article

VL - 86

SP - 1660

EP - 1672

JO - Journal of Statistical Computation and Simulation

JF - Journal of Statistical Computation and Simulation

SN - 0094-9655

IS - 9

ER -

ID: 146298923