Estimation of sex in forensic examinations using logistic regression and likelihood ratios

An overview of log likelihood ratio cost in forensic science - Where is it used and what values can we expect?

There is increasing support for reporting evidential strength as a likelihood ratio (LR) and increasing interest in (semi-)automated LR systems. The log-likelihood ratio cost (Cllr) is a popular metric for such systems, penalizing misleading LRs further from 1 more. Cllr = 0 indicates perfection while Cllr = 1 indicates an uninformative system. However, beyond this, what constitutes a "good" Cllr is unclear. Aiming to provide handles on when a Cllr is "good", we studied 136 publications on (semi-)automated LR systems. Results show Cllr use heavily depends on the field, e.g., being absent in DNA analysis. Despite more publications on automated LR systems over time, the proportion reporting Cllr remains stable. Noticeably, Cllr values lack clear patterns and depend on the area, analysis and dataset. As LR systems become more prevalent, comparing them becomes crucial. This is hampered by different studies using different datasets. We advocate using public benchmark datasets to advance the field.

A methodological comparison of discriminant function analysis and binary logistic regression for estimating sex in forensic research and case-work

The purpose of this study is to assess the accuracy of two multivariate statistical approaches for estimating sex from human external ear anthropometry, namely, discriminant function analysis (DFA) and binary logistic regression (BLR). A cross-sectional sample of 497 participants (233 males and 264 females) aged 18-35 years (24.42 ± 5.17) was obtained from Himachal Pradesh state of North India. Both the ears of the participants (994) were examined for anthropometric measurements. A total of 12 anthropometric measurements were taken independently on the left and right ear of each individual with the help of a pair of sliding calipers using a standard method. The sex of the population groups was discriminated against using binary logistic regression and discriminant function analysis. The predictive percentage of sex estimation computed from both the models were substantially the same, that is, 76.3% from DFA and 76.2% from BLR, with nearly comparable (∼0.02) sensitivity, specificity, positive predictive value, and negative predictive values, whereas the values of correct predicted percentage were 0.1% higher in DFA than BLR. Moreover, the other comparison metrics, such as classification error, B-index, and Matthews correlation coefficient indicated that both models performed equally well. The study highlighted that if the assumptions of the statistical methods are met, both methods are equally capable of discriminating the population depending on sex. The study recommends that the discriminant function analysis and binary logistic regression may be used synonymously in forensic research and case-work pertaining to the estimation of sex and various other forensic situations.

A Novel Missing Data Imputation Approach for Time Series Air Quality Data Based on Logistic Regression

Missing values in air quality datasets bring trouble to exploration and decision making about the environment. Few imputation methods aim at time series air quality data so that they fail to handle the timeliness of the data. Moreover, most imputation methods prefer low-missing-rate datasets to relatively high-missing-rate datasets. This paper proposes a novel missing data imputation method, called FTLRI, for time series air quality data based on the traditional logistic regression and a presented “first Five & last Three” model, which can explain relationships between disparate attributes and extract data that are extremely relevant, both in terms of time and attributes, to the missing data, respectively. To investigate the performance of FTLRI, it is benchmarked with five classical baselines and a new dynamic imputation method using a neural network with average hourly concentration data of pollutants from three disparate stations in Lanzhou in 2019 under different missing rates. The results show that FTLRI has a significant advantage over the compared imputation approaches, both in the particular short-term and long-term time series air quality data. Furthermore, FTLRI has good performance on datasets with a relatively high missing rate, since it only selects the data extremely related to the missing values instead of relying on all the other data like other methods.