Application Of Likelihood Ratios Research Articles

Forensic physical fits in the trace evidence discipline: A review

Physical fit examinations have long played a critical role in forensic science, particularly in the trace evidence, toolmark, and questioned documents disciplines. Specifically, in trace evidence, physical fits arise in various instances such as separated pieces of duct tape, torn textile fragments, and fractured polymeric items to name a few. The case report and research basis for forensic physical fit dates to the late 1700s and varies by material type. Three main areas of physical fit appear within the literature: case reports, fractography studies, and quantitative assessment of a fracture fit. A strong foundation within the discipline lies in case reports, articles demonstrating occurrences of physical fit the authors have experienced in their laboratories. Fractography research offers information about the fracturing mechanism of a given material for purposes of identifying a potential breaking source. Also, fractography studies demonstrate variation in fracture morphology per material types, with a qualitative basis for comparison and reporting. The current shift in the research appears to be more quantitative or performance-based, assessing the error rates associated with physical fit examinations, the application of likelihood ratios as a means to determine evidential weight, probabilistic interpretations of large sample sets, and the implementation of automatic edge-detection algorithms to support the examiner’s expert opinion. This review aims to establish the current state of physical fit research through what has been accomplished, the limitations faced due to the unpredictable nature of casework, and the future directions of the discipline. In addition, current practice in the field is evaluated through a review of standard operating procedures.

Application of likelihood ratios and optimal decision thresholds in fire debris analysis based on a partial least squares discriminant analysis (PLS-DA) model

A set of 9000 computationally generated (in-silico) total ion spectra comprising samples containing ignitable liquids from ASTM E1618-14 classes mixed with pyrolyzed substrates (class IL) and samples containing a mixture of pyrolyzed substrates without ignitable liquid contribution (class SUB) was used to generate a partial least squares discriminant analysis (PLS-DA) model. The training data set was balanced in IL and SUB class composition and the IL class samples were composed of a uniform distribution of ignitable liquids from the ASTM E1618-14 defined classes. The number of components in the optimized PLS-DA model, 13, was determined by optimizing the area under the receiver operating characteristic curve (ROC AUC) as a function of the number of components. Ten cross validation folds had an average ROC AUC of 0.930 ± 0.006. The PLS-DA model was further challenged with a uniformly distributed and balanced IL/SUB validation set of 1000 computationally mixed total ion spectra, giving an ROC AUC 0f 0.93. The model which had been validated on computationally generated total ion spectra was tested on a set of “real” fire debris samples from large-scale burn tests that had been independently evaluated by an “informed analyst”. The results from this study demonstrate the application of a chemometric model to the forensic analysis of fire debris data and emphasize the connection between quantified strength of the evidence and categorical decisions based on a defined operational decision point on the ROC curve. The success of the PLS-DA model demonstrates the feasibility of applying chemometric methods to the analysis of fire debris as a means of introducing probabilistic statements of evidentiary value to the court.

Application of likelihood ratio and posterior probability density in sex estimation from level two fingerprint features among Hausa ethnic group

BackgroundIn forensic sciences, the nature and variability of configurations of epidermal ridge minutiae has practically been explored in the evaluation and comparison of fingerprints, including partial fingerprints. The objectives of the present study were to determine the potential of fingerprint minutiae (level two features) in estimation of sex using likelihood ratio and posterior probability density among Hausa population. Secondly, to determine which of the minutiae have more of the estimation power compared to others.MethodsA total of 420 thumbprints from 210 participants were analyzed. The minutiae density was determined within a circle whose diameter cuts 18 ridges and passing through the nucleus of the fingerprints. Posterior probability density and likelihood ratio were used to estimate the sex.ResultsThe results showed that the median of minutiae count in males tends to be significantly higher in right ridge ending and ridge enclosure. Similarly, in the left thumb, the significantly higher minutiae count in males was observed in the ridge ending and ridge dot. In all the minutiae types, higher minutiae density favors males compared to the females. Likewise, in all the minutiae density, except for right ridge convergent and bifurcation and ridge ending, absence of any of the minutiae in the thumb favored female participants.ConclusionIncrease in minutiae density was likely to infer males more than females. Ridge ending minutiae was observed to have higher discriminating power shown by higher likelihood ratio for sex inference among Hausa population.

Large sample test criteria on Toeplitz covariance structure

ABSTRACTIn the analysis of stationary stochastic process, one has to deal with covariance matrix of Toeplitz (or Laurent) structure. Such structure has a feature that not only the elements on the principal diagonal but also those lying on each of the parallel sub-diagonals are equal as well. The present investigation is on the problem of large sample testing of the Toeplitz pattern of the population covariance matrix. Apart from usual application of likelihood ratio and Rao’s efficient score criteria, some heuristic two-stage tests are suggested. The results of Monte Carlo experiment are reported for the size of the proposed tests.

Uso de tests diagnósticos en la práctica clínica (Parte 2): Aplicación clínica y utilidad de un test diagnóstico

The clinical usefulness of a diagnostic test is determined by the extent to which it helps to modify the pretest probability of occurrence of a certain diagnosis. For this purpose, the calculation and application of likelihood ratios (LR) is a very useful tool. Every possible result of a test has a LR, which allows for an estimation of the magnitude by which the pretest probability increases or decreases, thereby helping the physician rule out, confirm, or continue investigating a diagnosis with new tests. In the first part of this article the properties of a diagnostic test (validity, reproducibility and safety) were analyzed. This second part illustrates the theoretical background underlying the concept of LR, explains the methodology for calculating them and describes their clinical application.

Evaluation of glass samples for forensic purposes — An application of likelihood ratios and an information–theoretical approach

This article presents an experimental study about the influence of the selection of an adequate database for evidence evaluation using chemical profiles. Evidence evaluation in the forensic sense can be seen as the comparison of two glass objects, one of known origin, denoted as control glass, and typically would be from the scene of a crime, and the other one of unknown origin, termed recovered glass, which might be found in association with a suspect. The aim is to obtain some estimate of the weight of evidence for the degree of support to any of the hypothesis in the case, typically these might be that the control and recovered glass come from the same source ( θ p ), and control and recovered glass come from different sources ( θ d ). A likelihood ratio is considered a suitable measure of the evidential weight for the competing propositions. The observations are of the elemental composition of glass, measured using a Scanning Electron Microscopy, coupled with an Energy Dispersive X-ray spectrometer technique. A number of glass objects have been analyzed and their chemical profiles form a database which represents several sources of variation. In this paper questions surrounding the choice of observations to make are addressed empirically by assessing the impact of building each model using a database different from the one using for comparison. The performance of each evidence evaluation method is assessed by classical methods such as Tippett plots, or more recent information–theoretical approaches such as empirical cross-entropy (ECE) plots. The results show that several of the compositional elements are very robust to the selection of the background database, namely; calcium, silicon and sodium observed in their oxide forms. We also show that the likelihood ratio computed with the combination of these variables show a remarkable discriminating power, and good calibration, allowing them to be employed for the calculation of the strength of evidence in forensic case work.

Application of Likelihood Ratio and Logistic Regression Models to Landslide Susceptibility Mapping Using GIS

For landslide susceptibility mapping, this study applied and verified a Bayesian probability model, a likelihood ratio and statistical model, and logistic regression to Janghung, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of IRS satellite imagery and field surveys; and a spatial database was constructed from topographic maps, soil type, forest cover, geology and land cover. The factors that influence landslide occurrence, such as slope gradient, slope aspect, and curvature of topography, were calculated from the topographic database. Soil texture, material, drainage, and effective depth were extracted from the soil database, while forest type, diameter, and density were extracted from the forest database. Land cover was classified from Landsat TM satellite imagery using unsupervised classification. The likelihood ratio and logistic regression coefficient were overlaid to determine each factor's rating for landslide susceptibility mapping. Then the landslide susceptibility map was verified and compared with known landslide locations. The logistic regression model had higher prediction accuracy than the likelihood ratio model. The method can be used to reduce hazards associated with landslides and to land cover planning.

Are reference limits for serum creatine kinase valid in detection of the carrier state for Duchenne muscular dystrophy?

We evaluated serum creatine kinase (CK) as an index to heterozygosity in Duchenne muscular dystrophy. When the 97.5th percentile of the CK normal reference interval was selected as the cutoff point, only 31% of 28 obligate carrier mothers and 24% of 43 mothers of simplex cases (those with only one occurrence of dystrophy in the kindred) exceeded this cutoff value. The outcome depended to some degree on the method used for establishing the reference limit for 379 presumably non-carrier, ambulatory women. The considerable overlap of CK activities between the control and carrier population as well as the non-gaussian distribution of the data permitted no satisfactory approach for differentiating these two populations. Neither the application of likelihood ratios, which evaluates a continuum of results without the dichotomy of a cutoff point, nor the application of predictive value based on sensitivity and specificity, which involves use of a cutoff value for decision making, provided a reliable estimate of carrier status. There was no significant difference (2 alpha = .19) between the median CK activity of obligate carrier mothers and mothers of simplex cases. The serum CK test does not provide data that either support or reject the Haldane hypothesis.