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 United Nations Development Programme'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).
| Original language | English |
|---|---|
| Journal | Journal of Statistical Computation and Simulation |
| Volume | 86 |
| Issue number | 9 |
| Pages (from-to) | 1660-1672 |
| ISSN | 0094-9655 |
| DOIs | |
| Publication status | Published - 12 Jun 2016 |
Keywords
- Faculty of Social Sciences
- sensitivity analysis
- uncertainty analysis
- Monte Carlo
- simulation coverage
- HDI