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Statistics

Overarching GOALS

CSAFE is committed to leveraging statistical methods developed in one field of application for use in forensic science, as appropriate. Through research methods, CSAFE professionals are assessing reliability of categorical conclusions, investigations of properties of machine learning algorithms, and studies of score-based likelihood ratios to inform multiple domains.

Hal Stern

Hal S. Stern

Provost and Executive Vice Chancellor and Chancellor's Professor, Co-Director of CSAFE

University of California, Irvine

DanicaOmmen_web

Danica Ommen

Associate Professor

Iowa State University

Additional Team Members

Naomi Kaplan-Damry nkapland@uci.edu

Alicia Carriquiry
alicia@iastate.edu

Heike Hofmann hofmann@iastate.edu

Steve Lund (NIST)
steven.lund@nist.gov

focus Areas

CSAFE researchers are using traditional logistic models to study the performance characteristics of individual examiners and individual examples, as well as aggregate performance characteristics for the population. We are aiming to learn about the efficiency of individual examiners and about the population of examiners.

In many forensic science disciplines, especially those involving pattern comparisons, the most common approach to analysis of the evidence involves a series of binary or categorical decisions regarding the evidence. For example, in latent print analysis an examiner initially decides about whether the latent print has enough information to make a formal identification, or not enough value (i.e., there is not enough information to perform the comparison). Following this, assuming the print is of value, the examiner will reach a final decision that is again expressed in categorical terms (e.g., identification, inconclusive, exclusion). There is currently considerable discussion about the role of likelihood ratios in the analysis of forensic evidence. The ENFSI guidelines endorse this approach. Ongoing discussion about the next steps in forensic pattern evidence analysis in the United States however suggests maintaining the focus on categorical outcomes, with perhaps more potential outcomes allowed (a 5-point or larger scale). To date evaluations of forensic examiners have focused primarily on binary decisions (did they correctly identify a pair of known matching items?). There is a need for developing statistical approaches to reliability and validity studies using categorical scales.  

The presumed setup for this research project is that data has been collected from a number of forensic science examiners on a number of cases or examples. For each examiner–example pair we have the outcome of the analysis (e.g., determination of value, conclusion with respect to source) on a categorical scale. There may also be data available about characteristics of the examiners and about characteristics of the examples. As a starting point for the research we will consider analyses treating each category as a binary response. This would, for example, in the latent print case, correspond to studying the probability of a VID (value for identification) decision (yes/no) and assessing variation in the decision-making process across examiners and examples. This can be done with traditional logistic models or with the closely related item response theory models used in educational testing. Using such models allows one to obtain information about the performance characteristics of individual examiners (and individual examples) as well as aggregate performance characteristics for the population. The next stage of the analysis will consider generalizations of these models to handle the multiple-category variables.  This will focus on multinomial models, including those developed by considering underlying latent continuous variables. The aim of these models, like those described above, is to learn about the efficiency of individual examiners and about the population of examiners.

The primary goals of the proposed project are to (1) explore the strengths and weaknesses of score-based likelihood ratios (SLRs) for quantifying the value of evidence from a statistical perspective, (2) explore the strengths and weaknesses of SLRs from the perspective of forensic evidence interpretation, and (3) determine whether it is possible to develop a framework of evidence interpretation which exploits the strengths of SLRs for impression and pattern evidence. This project would greatly benefit the forensic science community by providing those who wish to use SLRs with a list of recognized strengths and weaknesses, with supporting reasons, as well as a framework for expressing conclusions regarding the SLR results.

Score-based likelihood ratios (SLRs) are becoming increasingly popular for analyzing impression and pattern evidence due to the inherent difficulties in computing Bayes Factors. Some researchers have argued against the use of SLRs within a Bayesian decision paradigm for philosophical reasons, often citing a lack of coherence. Additionally, these researchers might argue that SLRs don’t actually approximate a Bayes Factor, and worse still, there is no indication of how far an SLR may be from the corresponding Bayes Factor. Other researchers have argued that there is no issue with using score-based likelihood ratios in a Bayesian decision paradigm as long as that SLR is accompanied by a measure of calibration of the SLR system. Regardless of which viewpoint one takes, the fact remains that very little research has been published on whether or not SLRs have any validity for quantifying the value of forensic evidence. The primary goals of the proposed project are to (1) explore the strengths and weaknesses of SLRs for quantifying the value of evidence from a statistical perspective, (2) explore the strengths and weaknesses of SLRs from the perspective of forensic evidence interpretation, and (3) determine whether it is possible to develop a framework of evidence interpretation which exploits the strengths of SLRs for impression and pattern evidence. Many forensic science researchers and practitioners have a strong desire for quantitative results for impression and pattern evidence to bolster their “subjective” opinions. This project would greatly benefit the forensic science community by providing those who wish to use SLRs with a list of recognized strengths and weaknesses, with supporting reasons, as well as a framework for expressing conclusions regarding the SLR results.

The primary goals of this project are to (1) explore the extent to which violating the assumption of independence affects the performance of the scoring methods and (2) develop machine learning methods for evaluating comparison scores for forensic evidence which can accommodate and/or adjust for the dependency in the data. The proposed research will impact the community by providing more statistically rigorous methods of computing score-based likelihood ratios for impression and pattern evidence.

Pattern and impression evidence results in data that is inherently high-dimensional and difficult to model statistically. Therefore, many researchers have focused on methods of measuring the similarity between two objects instead. This comparison results in a low-dimensional score which is much easier to model. CSAFE researchers have relied on statistical machine learning algorithms to compute the scores. One of the difficulties with these methods is that the pairwise comparison of all the evidential objects results in a set of dependent scores. This is because any of the scores that contain the same object as one of the two in the comparison will be dependent. The difficulty lies in the fact that while machine learning methods do not have any distributional assumptions, most assume independence between the observations in the data. The primary goals of this project are to (1) explore the extent to which violating the assumption of independence affects the performance of the scoring methods and (2) develop machine learning methods for evaluating comparison scores for forensic evidence that can accommodate and/or adjust for the dependency in the data. The proposed research will impact the community by providing more statistically rigorous methods of computing score-based likelihood ratios for impression and pattern evidence. This project builds on the work achieved during the first five years in Project CC, “Statistical and Algorithmic Approaches to Matching Bullets” and in Project EE, “Statistical and Algorithmic Approaches to Shoeprint Analysis,” by critically evaluating the current methods for violations of assumptions and potential areas for correction and improvement before the current methods are deployed in crime labs.

Knowledge Transfer

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Found 92 Results
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Camera Device Identification and the Effects of Underexposure

Type: , Research Area(s): ,

Published: 2023 | By: Seth Pierre

Technology today allows a photograph from a digital camera to be matched with the camera that took it. However, the matching software was created over 10 years ago using data that is not necessarily representative of today’s data. The objective…

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Shifting decision thresholds can undermine the probative value and legal utility of forensic pattern-matching evidence

Type: Research Area(s): ,

Published: 2023 | By: William Thompson

Forensic pattern analysis requires examiners to compare the patterns of items such as fingerprints or tool marks to assess whether they have a common source. This article uses signal detection theory to model examiners’ reported conclusions (e.g., identification, inconclusive, or…

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A statistical approach to aid examiners in the forensic analysis of handwriting

Type: Research Area(s): ,

Published: 2023 | By: Amy Crawford

We develop a statistical approach to model handwriting that accommodates all styles of writing (cursive, print, connected print). The goal is to compute a posterior probability of writership of a questioned document given a closed set of candidate writers. Such…

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Ensemble learning for score likelihood ratios under the common source problem

Type: Research Area(s):

Published: 2023 | By: Federico Veneri

Machine learning-based score likelihood ratios (SLRs) have emerged as alternatives to traditional likelihood ratios and Bayes factors to quantify the value of evidence when contrasting two opposing propositions. When developing a conventional statistical model is infeasible, machine learning can be…

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A method for quantifying individual decision thresholds of latent print examiners

Type: Research Area(s): ,

Published: 2023 | By: Amanda Luby

In recent years, ‘black box’ studies in forensic science have emerged as the preferred way to provide information about the overall validity of forensic disciplines in practice. These studies provide aggregated error rates over many examiners and comparisons, but errors…

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The q–q Boxplot

Type: Research Area(s):

Published: 2021 | By: Jordan Rodu

Boxplots have become an extremely popular display of distribution summaries for collections of data, especially when we need to visualize summaries for several collections simultaneously. The whiskers in the boxplot show only the extent of the tails for most of…

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The Contribution of Forensic and Expert Evidence to DNA Exoneration Cases: An Interim Report

Type: , Research Area(s): ,,

Published: 2023 | By: Simon Cole

This report is from Simon A. Cole, Vanessa Meterko, Sarah Chu, Glinda Cooper, Jessica Weinstock Paredes, Maurice Possley, and Ken Otterbourg (2022), The Contribution of Forensic and Expert Evidence to DNA Exoneration Cases: An Interim Report (National Registry of Exonerations…

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Likelihood ratios for categorical count data with applications in digital forensics

Type: Research Area(s): ,

Published: 2022 | By: Rachel Longjohn

We consider the forensic context in which the goal is to assess whether two sets of observed data came from the same source or from different sources. In particular, we focus on the situation in which the evidence consists of…

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CSAFE Project Update & ASCLD FRC Collaboration

Type: Research Area(s): ,,,,,

Published: 2022 | By: Jeff Salyards

This presentation highlighted CSAFE's collaboration with the ASCLD FRC Collaboration Hub.

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Reliability for Binary and Ordinal Data in Forensics

Type: Research Area(s):

Published: 2022 | By: Hina Arora

Black-box studies are a crucial part of assessing the accuracy and reliability of subjective decisions in forensics. The extant black-box studies have generally had two components. In the first study, examiners are asked to assess forensic samples (typically questioned and…

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Uncertainty in probabilistic genotyping of low template DNA: A case study comparing STRMix™ and TrueAllele™

Type: Research Area(s): ,

Published: 2023 | By: William Thompson

Two probabilistic genotyping (PG) programs, STRMix™ and TrueAllele™, were used to assess the strength of the same item of DNA evidence in a federal criminal case, with strikingly different results. For STRMix, the reported likelihood ratio in favor of the…

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Understanding forensic decision-making with Item Response Theory: Using a NFI firearms study

Type: Research Area(s): ,

Published: 2022 | By: Amanda Luby

This presentation is from the Forensic Big Data Colloquium at the Netherlands Forensic Institute, November 2022. Posted with permission of CSAFE.

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Source identification of shoeprints in mock crime scene using an algorithm based on automatic alignment

Type: Research Area(s): ,

Published: 2023 | By: Hana Lee

This presentation is from the 75th Anniversary Conference of the American Academy of Forensic Sciences, Orlando, Florida, February 13-18, 2023. Posted with permission of CSAFE

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Ensemble of Score Likelihood Ratios under the common source problem

Type: Research Area(s):

Published: 2023 | By: Frederico Veneri

Machine learning-based Score Likelihood Ratios have been proposed as an alternative to traditional Likelihood Ratios and Bayes Factor to quantify the value of evidence when contrasting two opposing propositions. Under the common source problem, the opposing proposition relates to the…

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Source Camera Identification on Multi-Camera Phones

Type: Research Area(s): ,

Published: 2023 | By: Stephanie Reinders

Camera identification addresses the scenario where an investigator has a questioned digital image from an unknown camera. The investigator wants to know whether the questioned image was taken by a camera on a person of interest’s phone. Researchers discovered that…

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Twin Convolutional Neural Networks to Classify Writers Using Handwriting Data

Type: Research Area(s): ,

Published: 2023 | By: Pilhyun (Andrew) Lim

Identifying the source of handwriting is an important application in the field of forensic science that addresses questioned document evidence found in criminal cases and civil litigation. It is difficult, given the idiosyncrasies of a person’s handwriting, to recognize the…

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Quantifying Bayes Factors for Forensic Handwriting Evidence

Type: Research Area(s): ,

Published: 2023 | By: Anyesha Ray

Questioned Document Examiners (QDEs) are tasked with analyzing handwriting evidence to make source (or writership) determinations. The Center for Statistics and Applications of Forensic Evidence (CSAFE) has previously developed computational methods to automatically extract quantifiable handwriting features and statistical methods…

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Quantifying Writer Variance Through Rainbow Triangle Graph Decomposition of the Common Word “the”

Type: Research Area(s): ,

Published: 2023 | By: Alexandria Arabio

Handwriting comparative analysis is based on the principle that no two individuals can produce the same writing and that an individual cannot exactly reproduce his/her handwriting. This project aims to assess and quantify the natural variations produced by a distinct…

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Sampling & Non-Response: Implications for inference in black-box studies

Type: Research Area(s): ,

Published: 2023 | By: Kori Khan

The following is from University of California Law San Francisco symposium "Forensic identification in criminal courts," February 2023. Posted with permission of CSAFE.

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Analyzing spatial responses: A comparison of IRT- based approaches

Type: Research Area(s): ,

Published: 2023 | By: Amanda Luby

We investigate two approaches for analyzing spatial coordinate responses using models inspired by Item Response Theory (IRT). In the first, we use a two-stage approach to first construct a pseudoresponse matrix using the spatial information and then apply standard IRT…

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