Density estimation of simulation output using exponential epi-splines

Dashi I. Singham, Johannes O. Royset, Roger J-B Wets
<span title="">2013</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7n2gtrr2tzevzonkaymviwxhsa" style="color: black;">2013 Winter Simulations Conference (WSC)</a> </i> &nbsp;
The density of stochastic simulation output provides more information on system performance than the mean alone. However, density estimation methods may require large sample sizes to achieve a certain accuracy or desired structural properties. A nonparametric estimation method based on exponential epi-splines has shown promise to overcome this difficulty by incorporating qualitative and quantitative information that reduces the space of possible density estimates substantially. Such 'soft'
more &raquo; ... mation may come in the form of the knowledge of a non-negative support, unimodality, and monotonicity, and is often available in simulation applications. We examine this method for output analysis of stochastic systems with fixed input parameters, and for a model with stochastic input parameters, with an emphasis on the use of derivative information.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/wsc.2013.6721468">doi:10.1109/wsc.2013.6721468</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/wsc/SinghamRW13.html">dblp:conf/wsc/SinghamRW13</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ghxllhwih5cz3bztmoi2asy54e">fatcat:ghxllhwih5cz3bztmoi2asy54e</a> </span>
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