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We study a multi-item inventory system with normally distributed demands in the presence of demand parameter uncertainty - the uncertainty that stems from the estimation of the unknown demand parameters necessitated by limited amounts of historical demand data. Using an asymptotic normality approximation, we quantify the variance of the demand fulfillment probability (i.e., the probability that all...
The normality assumption concerning the distribution of equity returns has long been challenged both empirically and theoretically. Alternative distributions have been proposed to better capture the characteristics of equity return data. This paper investigates the ability of five alternative distributions to represent the behavior of daily equity index returns over the period 1979–2014: the skewed...
We study a subset selection procedure - one of the well-known statistical methods of ranking and selection for stochastic simulations - in the presence of input parameter uncertainty; i.e., the parameters of the input distributions are unknown and there is only a limited amount of input data available for input parameter estimation. The goal is to present a new decision rule which identifies subsets...
This paper considers a stochastic system simulation with unknown input distribution parameters and assumes the availability of a limited amount of historical data for parameter estimation. We investigate how to account for parameter uncertainty — the uncertainty that is due to the estimation of the input distribution parameters from historical data of finite length — in a subset selection procedure...
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