Evaluation of Forensic Data Using Logistic Regression-Based Classification Methods and an R Shiny Implementation.

We demonstrate the use of classification methods that are well-suited for forensic toxicology applications. The methods are based on penalized logistic regression, can be employed when separation occurs in a two-class classification setting, and allow for the calculation of likelihood ratios. A case study of this framework is demonstrated on alcohol biomarker data for classifying chronic alcohol drinkers.

Hybrid approach combining chemometrics and likelihood ratio framework for reporting the evidential value of spectra.

Many chemometric tools are invaluable and have proven effective in data mining and substantial dimensionality reduction of highly multivariate data. This becomes vital for interpreting various physicochemical data due to rapid development of advanced analytical techniques, delivering much information in a single measurement run. This concerns especially spectra, which are frequently used as the subject of comparative analysis in e.

The prevalence of mixed DNA profiles on fingernail swabs.

There is a general acceptance that cellular material will transfer from one person to another person's fingernails through everyday contact. However, the level or degree of contact required to transfer sufficient cellular material in order to obtain a DNA profile is not known. This study examined swabs from the fingernails of 40 volunteers and compared the DNA profiles obtained to the daily activities of that individual.

A two-level model for evidence evaluation in the presence of zeros.

Likelihood ratios (LRs) provide a natural way of computing the value of evidence under competing propositions. We propose LR models for classification and comparison that extend the ideas of Aitken, Zadora, and Lucy and Aitken and Lucy to include consideration of zeros. Instead of substituting zeros by a small value, we view the presence of zeros as informative and model it using Bernoulli distributions.

Partially linear censored quantile regression.

Censored regression quantile (CRQ) methods provide a powerful and flexible approach to the analysis of censored survival data when standard linear models are felt to be appropriate. In many cases however, greater flexibility is desired to go beyond the usual multiple regression paradigm. One area of common interest is that of partially linear models: one (or more) of the explanatory covariates are assumed to act on the response through a non-linear function.