Statistical Analysis in Forensic Science

Evidential Value of Multivariate Physicochemical Data

Inbunden, Engelska, 2014

AvGrzegorz Zadora,Agnieszka Martyna,Daniel Ramos,Colin Aitken

1 409 kr

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A practical guide for determining the evidential value of physicochemical data Microtraces of various materials (e.g. glass, paint, fibres, and petroleum products) are routinely subjected to physicochemical examination by forensic experts, whose role is to evaluate such physicochemical data in the context of the prosecution and defence propositions. Such examinations return various kinds of information, including quantitative data. From the forensic point of view, the most suitable way to evaluate evidence is the likelihood ratio. This book provides a collection of recent approaches to the determination of likelihood ratios and describes suitable software, with documentation and examples of their use in practice. The statistical computing and graphics software environment R, pre-computed Bayesian networks using Hugin Researcher and a new package, calcuLatoR, for the computation of likelihood ratios are all explored.Statistical Analysis in Forensic Science will provide an invaluable practical guide for forensic experts and practitioners, forensic statisticians, analytical chemists, and chemometricians.Key features include: Description of the physicochemical analysis of forensic trace evidence.Detailed description of likelihood ratio models for determining the evidential value of multivariate physicochemical data.Detailed description of methods, such as empirical cross-entropy plots, for assessing the performance of likelihood ratio-based methods for evidence evaluation.Routines written using the open-source R software, as well as Hugin Researcher and calcuLatoR.Practical examples and recommendations for the use of all these methods in practice.

Produktinformation

Utgivningsdatum2014-01-24
Mått176 x 252 x 22 mm
Vikt680 g
FormatInbunden
SpråkEngelska
Antal sidor336
FörlagJohn Wiley & Sons Inc
ISBN9780470972106

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Preface xiii1 Physicochemical data obtained in forensic science laboratories 11.1 Introduction 11.2 Glass 21.3 Flammable liquids: ATD-GC/MS technique 81.4 Car paints: Py-GC/MS technique 101.5 Fibres and inks: MSP-DAD technique 13References 152 Evaluation of evidence in the form of physicochemical data 192.1 Introduction 192.2 Comparison problem 212.3 Classification problem 272.4 Likelihood ratio and Bayes’ theorem 31References 323 Continuous data 353.1 Introduction 353.2 Data transformations 373.3 Descriptive statistics 393.4 Hypothesis testing 593.5 Analysis of variance 783.6 Cluster analysis 853.7 Dimensionality reduction 92References 1054 Likelihood ratio models for comparison problems 1074.1 Introduction 1074.2 Normal between-object distribution 1084.3 Between-object distribution modelled by kernel density estimation 1104.4 Examples 1124.5 R Software 140References 1495 Likelihood ratio models for classification problems 1515.1 Introduction 1515.2 Normal between-object distribution 1525.3 Between-object distribution modelled by kernel density estimation 1555.4 Examples 1575.5 R software 172References 1796 Performance of likelihood ratio methods 1816.1 Introduction 1816.2 Empirical measurement of the performance of likelihood ratios 1826.3 Histograms and Tippett plots 1836.4 Measuring discriminating power 1866.5 Accuracy equals discriminating power plus calibration: Empirical cross-entropy plots 1926.6 Comparison of the performance of different methods for LR computation 2006.7 Conclusions: What to measure, and how 2146.8 Software 215References 216Appendix A Probability 218A.1 Laws of probability 218A.2 Bayes’ theorem and the likelihood ratio 222A.3 Probability distributions for discrete data 225A.4 Probability distributions for continuous data 227References 227Appendix B Matrices: An introduction to matrix algebra 228B.1 Multiplication by a constant 228B.2 Adding matrices 229B.3 Multiplying matrices 230B.4 Matrix transposition 232B.5 Determinant of a matrix 232B.6 Matrix inversion 233B.7 Matrix equations 235B.8 Eigenvectors and eigenvalues 237Reference 239Appendix C Pool adjacent violators algorithm 240References 243Appendix D Introduction to R software 244D.1 Becoming familiar with R 244D.2 Basic mathematical operations in R 246D.3 Data input 252D.4 Functions in R 254D.5 Dereferencing 255D.6 Basic statistical functions 257D.7 Graphics with R 258D.8 Saving data 266D.9 R codes used in Chapters 4 and 5 266D.10 Evaluating the performance of LR models 289Reference 293Appendix E Bayesian network models 294E.1 Introduction to Bayesian networks 294E.2 Introduction to Hugin ResearcherTM software 296References 308Appendix F Introduction to calcuLatoR software 309F.1 Introduction 309F.2 Manual 309Reference 314Index 315