A Robust Approach to Automatically Locating Grooves in 3D Bullet Land Scans

Land engraved areas (LEAs) provide evidence to address the same source–different source problem in forensic firearms examination. Collecting 3D images of bullet LEAs requires capturing portions of the neighboring groove engraved areas (GEAs). Analyzing LEA and GEA data separately is imperative to accuracy in automated comparison methods such as the one developed by Hare et al. (Ann Appl Stat 2017;11, 2332). Existing standard statistical modeling techniques often fail to adequately separate LEA and GEA data due to the atypical structure of 3D bullet data. We developed a method for automated removal of GEA data based on robust locally weighted regression (LOESS). This automated method was tested on high‐resolution 3D scans of LEAs from two bullet test sets with a total of 622 LEA scans. Our robust LOESS method outperforms a previously proposed “rollapply” method. We conclude that our method is a major improvement upon rollapply, but that further validation needs to be conducted before the method can be applied in a fully automated fashion

rotations: An R Package for SO(3) Data

Abstract In this article we introduce the rotations package which provides users with the ability to simulate, analyze and visualize three-dimensional rotation data. More specifically it includes four commonly used distributions from which to simulate data, four estimators of the central orientation, six confidence region estimation procedures and two approaches to visualizing rotation data. All of these features are available for two different parameterizations of rotations: three-by-three matrices and quaternions. In addition, two datasets are included that illustrate the use of rotation data in practice

A Permutation Test for Correlated Errors in Adjacent Questionnaire Items

Response patterns are of importance to survey researchers because of the insight they provide into the thought processes respondents use to answer survey questions. In this paper we propose the use of structural equation modeling to examine response patterns and develop a permutation test to quantify the likelihood of observing a specific response pattern. Of interest is a response pattern where the response to the current item is conditioned on the respondent’s answer to the immediately preceding item. This pattern manifests itself in the error structure of the survey items by resulting in larger correlations of the errors for adjacent items than for non-adjacent items. We illustrate the proposed method using data from the 2002 Oregon Survey of Roads and Highways and report SAS code which can be easily modified to examine other response patterns of interest.Keywords: permutation test, general-specific questions, correlated errors, response patterns in survey

The Power in Groups: Using Cluster Analysis to Critically Quantify Women’s STEM Enrollment

Despite efforts to close the gender gap in science, technology, engineering, and math (STEM), disparities still exist, especially in math intensive STEM (MISTEM) majors. Females and males receive similar academic preparation and overall, perform similarly, yet females continue to enroll in STEM majors less frequently than men. In examining academic preparation, most research considers performance measures individually, ignoring the possible interrelationships between these measures. We address this problem by using hierarchical agglomerative clustering – a statistical technique which allows for identifying groups (i.e., clusters) of students who are similar in multiple factors. We first apply this technique to readily available institutional data to determine if we could identify distinct groups. Results illustrated that it was possible to identify nine unique groups. We then examined differences in STEM enrollment by group and by gender. We found that the proportion of females differed by group, and the gap between males and females also varied by group. Overall, males enrolled in STEM at a higher proportion than females and did so regardless of the strength of their academic preparation. Our results provide a novel yet feasible approach to examining gender differences in STEM enrollment in postsecondary education