Grzegorz Zadora, Agnieszka Martyna, Daniel Ramos, Colin Aitken

Statistical Analysis in Forensic Science

Evidential Value of Multivariate Physicochemical Data
By Zadora, Grzegorz; Martyna, Agnieszka; Ramos, Daniel; Aitken, Colin

• Gebundenes Buch

Produktbeschreibung

This book provides the methods and software to enable statisticians and forensic experts to work effectively with evidence evaluation methods. It also covers a collection of recent likelihood ratio (LR) approaches, explores suitable software toolboxes, and includes documentation and examples about how to use them in practice. R, pre-computed Bayesian networks for Hugin Researcher, and a new multiplatform software for LR computation named "calcuLatoR" are explored. The models in this book can also be applied in other applications of analytical chemistry, making it useful for chemometricians as well.

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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.

Produktdetails

• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 336
• Erscheinungstermin: 3. Februar 2014
• Englisch
• Abmessung: 250mm x 175mm x 23mm
• Gewicht: 766g
• ISBN-13: 9780470972106
• ISBN-10: 0470972106
• Artikelnr.: 39172072

Autorenporträt

Grzegorz Zadora, Institute of Forensic Research, Krakow, Poland. Daniel Ramos, Telecommunication Engineering, Universidad Autonoma de Madrid, Spain.

Inhaltsangabe

Preface xiii 1 Physicochemical data obtained in forensic science
laboratories 1 1.1 Introduction 1 1.2 Glass 2 1.3 Flammable liquids:
ATD-GC/MS technique 8 1.4 Car paints: Py-GC/MS technique 10 1.5 Fibres and
inks: MSP-DAD technique 13 References 15 2 Evaluation of evidence in the
form of physicochemical data 19 2.1 Introduction 19 2.2 Comparison problem
21 2.3 Classification problem 27 2.4 Likelihood ratio and Bayes' theorem 31
References 32 3 Continuous data 35 3.1 Introduction 35 3.2 Data
transformations 37 3.3 Descriptive statistics 39 3.4 Hypothesis testing 59
3.5 Analysis of variance 78 3.6 Cluster analysis 85 3.7 Dimensionality
reduction 92 References 105 4 Likelihood ratio models for comparison
problems 107 4.1 Introduction 107 4.2 Normal between-object distribution
108 4.3 Between-object distribution modelled by kernel density estimation
110 4.4 Examples 112 4.5 R Software 140 References 149 5 Likelihood ratio
models for classification problems 151 5.1 Introduction 151 5.2 Normal
between-object distribution 152 5.3 Between-object distribution modelled by
kernel density estimation 155 5.4 Examples 157 5.5 R software 172
References 179 6 Performance of likelihood ratio methods 181 6.1
Introduction 181 6.2 Empirical measurement of the performance of likelihood
ratios 182 6.3 Histograms and Tippett plots 183 6.4 Measuring
discriminating power 186 6.5 Accuracy equals discriminating power plus
calibration: Empirical cross-entropy plots 192 6.6 Comparison of the
performance of different methods for LR computation 200 6.7 Conclusions:
What to measure, and how 214 6.8 Software 215 References 216 Appendix A
Probability 218 A.1 Laws of probability 218 A.2 Bayes' theorem and the
likelihood ratio 222 A.3 Probability distributions for discrete data 225
A.4 Probability distributions for continuous data 227 References 227
Appendix B Matrices: An introduction to matrix algebra 228 B.1
Multiplication by a constant 228 B.2 Adding matrices 229 B.3 Multiplying
matrices 230 B.4 Matrix transposition 232 B.5 Determinant of a matrix 232
B.6 Matrix inversion 233 B.7 Matrix equations 235 B.8 Eigenvectors and
eigenvalues 237 Reference 239 Appendix C Pool adjacent violators algorithm
240 References 243 Appendix D Introduction to R software 244 D.1 Becoming
familiar with R 244 D.2 Basic mathematical operations in R 246 D.3 Data
input 252 D.4 Functions in R 254 D.5 Dereferencing 255 D.6 Basic
statistical functions 257 D.7 Graphics with R 258 D.8 Saving data 266 D.9 R
codes used in Chapters 4 and 5 266 D.10 Evaluating the performance of LR
models 289 Reference 293 Appendix E Bayesian network models 294 E.1
Introduction to Bayesian networks 294 E.2 Introduction to Hugin
ResearcherTM software 296 References 308 Appendix F Introduction to
calcuLatoR software 309 F.1 Introduction 309 F.2 Manual 309 Reference 314
Index 315