Centro de Documentação da PJ
Monografia
 34516 | (LPC-GAE) |  |
Statistical analysis in forensic science : evidential value of multivariate physicochemical data / Grzegorz Zadora.. [et al.].- 1st ed.- West Sussex : John Wiley & Sons, 2014.- 322 p. ; 25 cm
ISBN 978-0-470-97210-6
ESTATÍSTICA, ANÁLISE CRIMINAL, CIÊNCIA FORENSE
Preface. 1 Physicochemical data obtained in forensic science laboratories. 1.1 Introduction. 1.2 Glass. 1.3 Flammable liquids: ATD-GC/MS technique. 1.4 Car paints: Py-GC/MS technique. 1.5 Fibres and inks: MSP-DAD technique. References. 2 Evaluation of evidence in the form of physicochemical data. 2.1 Introduction. 2.2 Comparison problem. 2.3 Classification problem. 2.4 Likelihood ratio and Bayes’ theorem. References.3 Continuous data. 3.1 Introduction. 3.2 Data transformations. 3.3 Descriptive statistics. 3.4 Hypothesis testing. 3.5 Analysis of variance. 3.6 Cluster analysis. 3.7 Dimensionality reduction. References. 4 Likelihood ratio models for comparison problems. 4.1 Introduction. 4.2 Normal between-object distribution. 4.3 Between-object distribution modelled by kernel density estimation. 4.4 Examples. 4.5 R Software. References. 5 Likelihood ratio models for classification problems. 5.1 Introduction. 5.2 Normal between-object distribution. 5.3 Between-object distribution modelled by kernel density estimation. 5.4 Examples. 5.5 R software. References. 6 Performance of likelihood ratio methods. 6.1 Introduction. 6.2 Empirical measurement of the performance of likelihood ratios. 6.3 Histograms and Tippett plots. 6.4 Measuring discriminating power. 6.5 Accuracy equals discriminating power plus calibration: Empirical cross-entropy plots. 6.6 Comparison of the performance of different methods for LR computation. 6.7 Conclusions: What to measure, and how. 6.8 Software. References. Appendix A Probability. A.1 Laws of probability. A.2 Bayes’ theorem and the likelihood ratio. A.3 Probability distributions for discrete data. A.4 Probability distributions for continuous data. References. Appendix B Matrices: An introduction to matrix algebra. B.1 Multiplication by a constant. B.2 Adding matrices. B.3 Multiplying matrices. B.4 Matrix transposition. B.5 Determinant of a matrix. B.6 Matrix inversion. B.7 Matrix equations. B.8 Eigenvectors and eigenvalues. Reference. Appendix C Pool adjacent violators algorithm. References. Appendix D Introduction to R software. D.1 Becoming familiar with R. D.2 Basic mathematical operations in R. D.3 Data input. D.4 Functions in R. D.5 Dereferencing. D.6 Basic statistical functions. D.7 Graphics with R. D.8 Saving data. D.9 R codes used in Chapters 4 and 5. D.10 Evaluating the performance of LR models. Reference. Appendix E Bayesian network models. E.1 Introduction to Bayesian networks. E.2 Introduction to Hugin ResearcherTM software. References. Appendix F Introduction to calcuLatoR software. F.1 Introduction. F.2 Manual. Reference.