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