Quality and Reliability Engineering International

Circular prediction regions are used in ballistic testing to express the uncertainty in shot accuracy. We compare two modeling approaches for estimating circular prediction regions for the miss distance of a ballistic projectile. The miss distance response variable is bivariate normal and has a mean and variance that can change with one or more experimental factors. The first approach fits a heteroskedastic linear model using restricted maximum likelihood, and uses the Kenward-Roger statistic to estimate circular prediction regions....

Reliability experiments are important for determining which factors drive product reliability. The data collected in these experiments can be challenging to analyze. Often, the reliability or lifetime data collected follow distinctly nonnormal distributions and include censored observations. Additional challenges in the analysis arise when the experiment is executed with restrictions on randomization. The focus of this paper is on the proper analysis of reliability data collected from a nonrandomized reliability experiments....

Reliability tests determine which factors drive system reliability. Often, the reliability or failure time data collected in these tests tend to follow distinctly non- normal distributions and include censored observations. The experimental design should accommodate the skewed nature of the response and allow for censored observations, which occur when systems under test do not fail within the allotted test time. To account for these design and analysis considerations, Monte Carlo simulations are frequently used to evaluate experimental design properties....

This paper investigates regularization for continuously observed covariates that resemble step functions. The motivating examples come from operational test data from a recent United States Department of Defense (DoD) test of the Shadow Unmanned Air Vehicle system. The response variable, quality of video provided by the Shadow to friendly ground units, was measured on an ordinal scale continuously over time. Functional covariates, altitude and distance, can be well approximated by step functions....

In this paper we compare data from a fairly large legacy wind tunnel test campaign to smaller, statistically-motivated experimental design strategies. The comparison, using Monte Carlo sampling methodology, suggests a tremendous opportunity to reduce wind tunnel test efforts without losing test information. Suggested Citation Hill, Raymond R., Derek A. Leggio, Shay R. Capehart, and August G. Roesener. “Examining Improved Experimental Designs for Wind Tunnel Testing Using Monte Carlo Sampling Methods.” Quality and Reliability Engineering International 27, no....

We illustrate the construction of Bayesian D-optimal designs for nonlinear models and compare the relative efficiency of standard designs with these designs for several models and prior distributions on the parameters. Through a relative efficiency analysis, we show that standard designs can perform well in situations where the nonlinear model is intrinsically linear. However, if the model is nonlinear and its expectation function cannot be linearized by simple transformations, the nonlinear optimal design is considerably more efficient than the standard design....