We formulate and evaluate the Automated Simulation Analysis Procedure (ASAP), an algorithm for steady-state simulation output analysis based on the method of nonoverlapping batch means (NOBM). ASAP delivers a confidence interval for an expected response that is centered on the sample mean of a portion of a simulation-generated time series and satisfies a user-specified absolute or relative prec...

We study in this paper the following sequential decision procedure. First, the members of a group show their opinions on all the members, regarding a specific attribute. Taking into account this information, quasiarithmetic means and a family of thresholds, a subgroup of individuals is selected: those members whose collective assessments (obtained through a quasiarithmetic mean) reach a specifi...

In high-dimensional gene expression experiments such as microarray and RNA-seq experiments, the number of measured variables is huge while the number of replicates is small. As a consequence, hypothesis testing is challenging because the power of tests can be very low after controlling multiple testing error. Optimal testing procedures with high average power while controlling false discovery r...

We introduce ASAP2, an improved variant of the batchmeans algorithm ASAP for steady-state simulation output analysis. ASAP2 operates as follows: the batch size is progressively increased until the batch means pass the ShapiroWilk test for multivariate normality; and then ASAP2 delivers a correlation-adjusted confidence interval. The latter adjustment is based on an inverted Cornish-Fisher expan...