We introduce ASAP3, a refinement of the batch means algorithms ASAP and ASAP2, that delivers point and confidence-interval estimators for the expected response of a steady-state simulation. ASAP3 is a sequential procedure designed to produce a confidence-interval estimator that satisfies user-specified requirements on absolute or relative precision as well as coverage probability. ASAP3 operates as follows: the batch size is progressively increased until the batch means pass the Shapiro-Wilk test for multivariate normality; and then ASAP3 fits a first-order autoregressive (AR(1)) time series model to the batch means. If necessary, the batch size is further increased until the autoregressive parameter in the AR(1) model does not significantly exceed 0.8. Next, ASAP3 computes the terms of an inverse Cornish-Fisher expansion for the classical batch means t -ratio based on the AR(1) parameter estimates; and finally ASAP3 delivers a correlation-adjusted confidence interval based on this expansion. Regarding not only conformance to the precision and coverage-probability requirements but also the mean and variance of the half-length of the delivered confidence interval, ASAP3 compared favorably to other batch means procedures (namely, ABATCH, ASAP, ASAP2, and LBATCH) in an extensive experimental performance evaluation.

Abstract-To detect faults in a time-dependent process, we apply a discrete wavelet transform (DWT) to several independently replicated data sets generated by that process. The DWT can capture irregular data patterns such as sharp "jumps" better than the Fourier transform and standard statistical procedures without adding much computational complexity. Our wavelet coefficient selection method effectively balances model parsimony against data reconstruction error. The few selected wavelet coefficients serve as the "reduced-size" data set to facilitate an efficient decision-making method in situations with potentially large-volume data sets. We develop a general procedure to detect process faults based on differences between the reduced-size data sets obtained from the nominal (in-control) process and from a new instance of the target process that must be tested for an out-of-control condition. The distribution of the test statistic is constructed first using normal distribution theory and then with a new resampling procedure called "reversed jackknifing" that does not require any restrictive distributional assumptions. A Monte Carlo study demonstrates the effectiveness of these procedures. Our methods successfully detect process faults for quadrupole mass spectrometry samples collected from a rapid thermal chemical vapor deposition process.

Presently, Rotational Isomeric State (RIS) theory directly addresses polymer chain conformation as it relates to mechanical response trends. The primary goal of this work is to explore the adaptation of this methodology to the prediction of material stiffness. This multi-scale modeling approach relies on ionomer chain conformation and polymer morphology and thus has potential as both a predictive modeling tool and a synthesis guide. The Mark-Curro Monte Carlo methodology is applied to generate a statistically valid number of end-to-end chain lengths via RIS theory for four solvated Nafion cases. For each case, a probability density function for chain length is estimated using various statistical techniques, including the classically applied cubic spline approach. It is found that the stiffness prediction is sensitive to the fitting strategy. The significance of various fitting strategies, as they relate to the physical structure of the polymer, are explored so that a method suitable for stiffness prediction may be identified.

A multiscale modeling approach for the prediction of material stiffness of the ionic polymer Nafion is presented. Traditional rotational isomeric state theory is applied in combination with a Monte Carlo methodology to develop a simulation model of the conformation of Nafion polymer chains on a nanoscopic level from which a large number of end-to-end chain lengths are generated. The probability density function of end-to-end distances is then estimated and used as an input parameter to enhance existing energetics-based macroscale models of ionic polymer behavior. Several methods for estimating the probability density function are compared, including estimation using Johnson distributions, Bézier distributions, and cubic splines.

No abstract