Application of Nonparametric Binary Regression to Evaluate the Sensitivity of Explosives

Abstract

The performance of slapper detonators depends on the sensitivity of the explosive material within the detonators to a voltage stimulus. A series of experiments is typically used to estimate the sensitivity of the explosive material by subjecting individual detonators to various stimulus levels. The traditional methods for selecting stimulus levels and analyzing the resulting binary data are based on a parametric probit model that relates the probability of detonation to the level of the voltage stimulus. In this article, we explore an alternative method for analyzing the binary data using nonparametric regression. The use of this alternative method is illustrated by analyzing the ungrouped binary data resulting from a series of 25 tests that were used to characterize a single lot of detonators. We conclude that nonparametric regression can be very useful in visualizing the underlying relationship between a binary response and a covariate, even when the sample size is relatively small.

KEY WORDS:

• Adaptive orthogonal series estimation
• Sensitivity testing
• Ungrouped binary regression