Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA Bayesian Hierarchical Model for Evaluating Forensic Footwear Evidence
272
0
0.0
(
0
)
Added by
Neil A. Spencer
Publication date
2019
fields
Mathematical Statistics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
When a latent shoeprint is discovered at a crime scene, forensic analysts inspect it for distinctive patterns of wear such as scratches and holes (known as accidentals) on the source shoes sole. If its accidentals correspond to those of a suspects shoe, the print can be used as forensic evidence to place the suspect at the crime scene. The strength of this evidence depends on the random match probability---the chance that a shoe chosen at random would match the crime scene prints accidentals. Evaluating random match probabilities requires an accurate model for the spatial distribution of accidentals on shoe soles. A recent report by the Presidents Council of Advisors in Science and Technology criticized existing models in the literature, calling for new empirically validated techniques. We respond to this request with a new spatial point process model for accidental locations, developed within a hierarchical Bayesian framework. We treat the tread pattern of each shoe as a covariate, allowing us to pool information across large heterogeneous databases of shoes. Existing models ignore this information; our results show that including it leads to significantly better model fit. We demonstrate this by fitting our model to one such database.
rate research
Read More
We propose a hierarchical Bayesian model to estimate the proportional contribution of source populations to a newly founded colony. Samples are derived from the first generation offspring in the colony, but mating may occur preferentially among migrants from the same source population. Genotypes of the newly founded colony and source populations are used to estimate the mixture proportions, and the mixture proportions are related to environmental and demographic factors that might affect the colonizing process. We estimate an assortative mating coefficient, mixture proportions, and regression relationships between environmental factors and the mixture proportions in a single hierarchical model. The first-stage likelihood for genotypes in the newly founded colony is a mixture multinomial distribution reflecting the colonizing process. The environmental and demographic data are incorporated into the model through a hierarchical prior structure. A simulation study is conducted to investigate the performance of the model by using different levels of population divergence and number of genetic markers included in the analysis. We use Markov chain Monte Carlo (MCMC) simulation to conduct inference for the posterior distributions of model parameters. We apply the model to a data set derived from grey seals in the Orkney Islands, Scotland. We compare our model with a similar model previously used to analyze these data. The results from both the simulation and application to real data indicate that our model provides better estimates for the covariate effects.
Studying the determinants of adverse pregnancy outcomes like stillbirth and preterm birth is of considerable interest in epidemiology. Understanding the role of both individual and community risk factors for these outcomes is crucial for planning appropriate clinical and public health interventions. With this goal, we develop geospatial mixed effects logistic regression models for adverse pregnancy outcomes. Our models account for both spatial autocorrelation and heterogeneity between neighborhoods. To mitigate the low incidence of stillbirth and preterm births in our data, we explore using class rebalancing techniques to improve predictive power. To assess the informative value of the covariates in our models, we use posterior distributions of their coefficients to gauge how well they can be distinguished from zero. As a case study, we model stillbirth and preterm birth in the city of Philadelphia, incorporating both patient-level data from electronic health records (EHR) data and publicly available neighborhood data at the census tract level. We find that patient-level features like self-identified race and ethnicity were highly informative for both outcomes. Neighborhood-level factors were also informative, with poverty important for stillbirth and crime important for preterm birth. Finally, we identify the neighborhoods in Philadelphia at highest risk of stillbirth and preterm birth.
Preventing periodontal diseases (PD) and maintaining the structure and function of teeth are important goals for personal oral care. To understand the heterogeneity in patients with diverse PD patterns, we develop BAREB, a Bayesian repulsive biclustering method that can simultaneously cluster the PD patients and their tooth sites after taking the patient- and site- level covariates into consideration. BAREB uses the determinantal point process (DPP) prior to induce diversity among different biclusters to facilitate parsimony and interpretability. Since PD progression is hypothesized to be spatially-referenced, BAREB factors in the spatial dependence among tooth sites. In addition, since PD is the leading cause for tooth loss, the missing data mechanism is non-ignorable. Such nonrandom missingness is incorporated into BAREB. For the posterior inference, we design an efficient reversible jump Markov chain Monte Carlo sampler. Simulation studies show that BAREB is able to accurately estimate the biclusters, and compares favorably to alternatives. For real world application, we apply BAREB to a dataset from a clinical PD study, and obtain desirable and interpretable results. A major contribution of this paper is the Rcpp implementation of BAREB, available at https://github.com/YanxunXu/ BAREB.
Over the past decade, the field of forensic science has received recommendations from the National Research Council of the U.S. National Academy of Sciences, the U.S. National Institute of Standards and Technology, and the U.S. Presidents Council of Advisors on Science and Technology to study the validity and reliability of forensic analyses. More specifically, these committees recommend estimation of the rates of occurrence of erroneous conclusions drawn from forensic analyses. Black box studies for the various subjective feature-based comparison methods are intended for this purpose. In general, black box studies often have unbalanced designs, comparisons that are not independent, and missing data. These aspects pose difficulty in the analysis of the results and are often ignored. Instead, interpretation of the data relies on methods that assume independence between observations and a balanced experiment. Furthermore, all of these projects are interpreted within the frequentist framework and result in point estimates associated with confidence intervals that are confusing to communicate and understand. We propose to use an existing likelihood-free Bayesian inference method, called Approximate Bayesian Computation (ABC), that is capable of handling unbalanced designs, dependencies among the observations, and missing data. ABC allows for studying the parameters of interest without recourse to incoherent and misleading measures of uncertainty such as confidence intervals. By taking into account information from all decision categories for a given examiner and information from the population of examiners, our method also allows for quantifying the risk of error for the given examiner, even when no error has been recorded for that examiner. We illustrate our proposed method by reanalysing the results of the Noblis Black Box study by Ulery et al. in 2011.
Multiple systems estimation is a key approach for quantifying hidden populations such as the number of victims of modern slavery. The UK Government published an estimate of 10,000 to 13,000 victims, constructed by the present author, as part of the strategy leading to the Modern Slavery Act 2015. This estimate was obtained by a stepwise multiple systems method based on six lists. Further investigation shows that a small proportion of the possible models give rather different answers, and that other model fitting approaches may choose one of these. Three data sets collected in the field of modern slavery, together with a data set about the death toll in the Kosovo conflict, are used to investigate the stability and robustness of various multiple systems estimate methods. The crucial aspect is the way that interactions between lists are modelled, because these can substantially affect the results. Model selection and Bayesian approaches are considered in detail, in particular to assess their stability and robustness when applied to real modern slavery data. A new Markov Chain Monte Carlo Bayesian approach is developed; overall, this gives robust and stable results at least for the examples considered. The software and datasets are freely and publicly available to facilitate wider implementation and further research.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
