Handbook of forensic statistics / / edited by David L. Banks, Karen Kafadar, David H. Kaye, Maria Tackett

• Pubblicazione: Boca Raton : , : Chapman & Hall/CRC, , 2021
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (571 pages)
• Disciplina: 363.25015195; 519.502436325
• Soggetto topico: Forensic sciences - Statistical methods
• Persona (resp. second.): BanksDavid L.; KafadarKaren; KayeD. H <1947-> (David H.); TackettMaria
• Nota di contenuto: Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Foreword -- Preface -- Editors -- Contributors -- Section I: Perspectives on Forensic Statistics -- 1. The History of Forensic Inference and Statistics: A Thematic Perspective -- 1.1 Introduction -- 1.2 Forensic Science and the Evaluation of Evidence -- 1.3 The Need for an Interpretative Model -- 1.4 Support of Judicial Disciplines for a Scientific Presentation of the Value of Evidence -- 1.5 Probability of Proposition Given Evidence and of Evidence Given Proposition -- 1.6 Quantification of the Value of Evidence Using Alternative Numerical Summaries -- 1.7 Change from Two-Stage Approach to Continuous Approach -- 1.8 Presentation of Evidence: New Challenges to Solve -- 1.8.1 The Island Problem and Results of a Database Selection -- 1.8.2 Profile Probability vs Conditional Profile Probability -- 1.8.3 Evaluation by Taking Errors into Account -- 1.9 A Minimum Value for the Profile Probability -- 1.10 Propositions and Pre-Assessment -- 1.10.1 The Choice of Propositions -- 1.10.2 The Pre-Assessment -- 1.11 Translation of a Numerical Value into a Verbal Equivalent -- 1.12 Assessment of Performance -- 1.13 Role for Likelihood Ratio as aMeasure for Investigation as Well as for Evaluation -- 1.14 Probabilistic GraphicalModels -- 1.14.1 Bayesian Networks -- 1.14.2 Bayesian Networks to Manage 'Masses' of Evidence -- 1.14.3 Bayesian Networks in Judicial Contexts -- 1.14.4 Bayesian Networks in Forensic Science: Particular Case Modeling -- 1.14.5 Bayesian Networks in Forensic Science: Generic Patterns of Inference -- 1.15 Not Only Inference: The Way to Make a Decision -- 1.15.1 The Objectives and Ingredients of Decision Theory -- 1.15.2 Graphical Models -- 1.16 The Existence or Otherwise of a True Value of the Evidence -- Acknowledgments -- References. ; Section II: General Concepts andMethods -- 2. Frequentist Methods for Statistical Inference -- 2.1 Introduction -- 2.2 Definitions and Notation -- 2.2.1 Data and Evidence -- 2.3 Random Variables and Probability Distributions -- 2.3.1 Sampling from a Distribution or Population -- 2.4 Estimation -- 2.4.1 Properties of Point Estimators -- 2.4.2 Estimating Allele Proportions 2.4.2.1 A Point Estimate -- 2.4.2.2 Constructing a Confidence Interval -- 2.4.2.3 Choosing a Confidence Coefficient -- 2.4.3 Estimating a False Positive Probability Through an Experiment 2.4.3.1 The Design of Experiments to Test Categorical Source -- 2.4.3.2 An Experiment to Test Categorical Judgments of Latent Print Examiners -- 2.4.3.3 Constructing Confidence Intervals -- 2.4.4 Interpreting Confidence Intervals -- 2.5 p-Values -- 2.5.1 p-Values in a Comparison of Glass Fragments -- 2.5.2 Interpreting p-Values -- 2.6 Hypothesis Tests -- 2.6.1 Classical Hypothesis Tests for Refractive Index Matching 2.6.1.1 Type I Errors and the Size of a Test -- 2.6.1.2 Type II Errors and the Power of a Test -- 2.6.2 Hypothesis Testing with p-Values -- 2.6.3 Hypothesis Testing with Confidence Intervals -- 2.7 Issues in Interpreting the Results of Hypothesis Tests, p-Values, and Confidence Coefficients -- 2.7.1 Transposition -- 2.7.2 Multiple Tests: Proof of the Null Hypothesis and Adjusted p-Values -- 2.7.3 Arbitrary Lines -- 2.7.4 Alternatives and Likelihoods -- 2.8 Resampling Methods -- 2.8.1 Bootstrap Estimates -- 2.8.2 Permutation Tests -- Acknowledgments -- References -- 3. Bayesian Methods and Forensic Inference -- 3.1 Introduction -- 3.2 The Basics -- 3.2.1 A Beta-Binomial Mock Example -- 3.2.2 A Gamma-Poisson Mock Example -- 3.3 Markov Chain Monte Carlo -- 3.4 Broad Applications -- 3.5 Summary -- Acknowledgments -- References -- 4. Comparing Philosophies of Statistical Inference. ; 4.1 Inferential Philosophies -- 4.1.1 Frequentist Inference -- 4.1.2 Bayesian Inference -- 4.1.3 Other Approaches to Inference -- 4.1.3.1 Fiducial Inference -- 4.1.3.2 Likelihood Inference -- 4.1.3.3 Confidence Distributions -- 4.2 Comparing the Approaches -- 4.2.1 Planning Studies Using Frequentist Inference -- 4.2.2 Challenges for Frequentist Inference -- 4.2.3 Flexible Inference with Bayesian Methods -- 4.2.4 Model Modifications and Adjustments -- 4.2.5 The Prior Distribution and the Definition of Probability -- 4.3 Relevance to Forensic Statistics -- 4.3.1 Likelihood Ratios and Bayes Factors -- 4.3.2 Two-Stage Procedures in Forensic Science -- 4.3.3 Forensic Evidence as Expert Opinion and Error Rates -- 4.4 Summary -- References -- 5. Decision Theory -- 5.1 Introduction -- 5.2 Concepts of Statistical Decision Theory -- 5.2.1 Preliminaries: Basic Elements of Decision Problems -- 5.2.2 Utility Theory -- 5.2.3 Implications of the Expected Utility Maximisation Principle -- 5.2.4 The Loss Function -- 5.2.5 Particular Forms of the Expected Utility Maximisation Principle -- 5.2.6 Likelihood Ratios in the Decision Framework -- 5.3 Decision Theory in the Law and Forensic Science -- 5.3.1 Legal Applications -- 5.3.2 Forensic Science Applications 5.3.2.1 Forensic Identification -- 5.3.2.2 Understanding Probability Assignment as a Decision: The Use of Proper Scoring Rules -- 5.3.2.3 Other Forensic Decision Problems: Consignment Inspection -- 5.4 Discussion and Conclusions -- 5.5 Further Readings -- 5.5.1 Forensic Science -- 5.5.2 General -- Acknowledgments -- References -- 6. Association Does Not Imply Discrimination: Clarifying When Matches Are (and Are Not) Meaningful -- 6.1 Introduction -- 6.2 Association and Discrimination -- 6.2.1 Quality of Test: Sensitivity and Specificity -- 6.2.2 Sources of Error -- 6.2.3 Weight of Evidence: The Likelihood Ratio. ; 6.2.4 Useful Databases for Ascertaining Discriminatory Power -- 6.2.5 Conflating Conditional Statements: The Prosecutor's Fallacy -- 6.3 Examples: The Discriminatory Power of Forensic Evidence -- 6.3.1 Arson Investigation -- 6.3.2 Other Types of Forensic Evidence: DNA, Fingerprints, and Shoe Prints -- 6.3.3 Abusive Head Trauma -- 6.4 Conclusion -- References -- 7. Validation of Forensic Automatic Likelihood Ratio Methods -- 7.1 Introduction -- 7.1.1 Scope -- 7.1.2 Aim -- 7.1.3 Structure -- 7.2 Validation Process -- 7.2.1 Standardization -- 7.2.2 Validation of Theoretical and Empirical Aspects -- 7.2.3 Performance Characteristics for Automatic LR Methods -- 7.2.4 Empirical Validation -- 7.2.5 Validation Protocol -- 7.3 Primary Performance Characteristics -- 7.3.1 Performance of Probabilities by Proper Scoring Rules -- 7.3.2 Discrimination and Calibration of Probabilities -- 7.3.3 Performance of Likelihood Ratios -- 7.3.4 Properties of Well-Calibrated Likelihood Ratios -- 7.3.5 Examples with Primary Performance Characteristics -- 7.4 Secondary Performance Characteristics -- 7.4.1 Robustness -- 7.4.2 Monotonicity -- 7.4.3 Generalization -- 7.5 Conclusion -- References -- 8. Bayesian Networks in Forensic Science -- 8.1 Introduction -- 8.2 Probability Logic -- 8.3 Simple Bayesian Networks for Forensic Problems -- 8.4 Object-Oriented Bayesian Networks -- 8.5 Forensic Genetics -- 8.5.1 Bayesian Networks for Simple Criminal Identification -- 8.5.2 Simple Disputed Paternity -- 8.5.3 Bayesian Networks for Complex Criminal Cases Involving Family Relationships -- 8.5.4 Mutation -- 8.6 Bayesian Networks for Analysing Mixed DNA Profiles -- 8.6.1 Discrete Features -- 8.6.2 Continuous Features -- 8.7 Analysis of Sensitivity to Assumptions on Founder Genes -- 8.7.1 Uncertainty in Allele Frequencies -- 8.7.2 Heterogeneous Reference Population -- 8.8 Conclusions. ; Appendix 8A: Bayesian Network Basics -- 8A.1 Qualitative Structure -- 8A.2 Independence Properties -- 8A.3 Quantitative Structure -- 8A.4 Computation -- References -- Section III: Legal and Psychological Dimensions -- 9. How Well Do Lay People Comprehend Statistical Statements from Forensic Scientists? -- 9.1 Methodological Overview -- 9.2 Consistency -- 9.2.1 Framing -- 9.2.2 Format -- 9.3 Sensitivity -- 9.4 (In)Coherence -- 9.4.1 Prosecutor's Fallacy -- 9.4.2 Defense Attorney's Fallacy -- 9.4.3 Directional Errors -- 9.4.4 Aggregation Errors -- 9.5 Ability -- 9.6 Orthodoxy -- 9.7 Discussion -- 9.8 Conclusion -- References -- 10. Forensic Statistics in the Courtroom -- 10.1 The Purpose, Form, and Prerequisites of Expert Testimony -- 10.1.1 Lay and Expert Testimony -- 10.1.2 Qualifications for Statistical Experts (and Experts Who Use Statistics) -- 10.1.3 Forms of Statistical Expert Testimony -- 10.1.4 Reasonable Scientific or Statistical Certainty -- 10.2 Special Rules for Scientific Expert Testimony* -- 10.2.1 The General-Acceptance Standard -- 10.2.2 The Scientific-Validity Standard -- 10.3 Selected Evidentiary Issues in Forensic Statistics -- 10.3.1 Two Uses of Statistical Analysis as Evidence -- 10.3.2 Theory and Application -- 10.3.3 Error Rates in Determining Admissibility -- 10.3.4 Error Rates, Likelihood Ratios, and Bayes Factors for Quantifying Probative Value -- 10.4 Conclusion -- References -- Cases and Rules -- Section IV: Applications of Statistics to Particular Fields in Forensic Science -- 11. DNA Frequencies and Probabilities -- 11.1 Introduction -- 11.2 Likelihood Ratios -- 11.3 Population Genetics -- 11.3.1 Single Loci -- 11.3.2 Multiple Loci -- 11.3.3 Population Structure -- 11.3.4 Lineage Markers -- 11.4 Mixtures -- 11.4.1 Semi-Continuous Model -- 11.4.2 Continuous Model -- 11.5 DNA Sequence Data -- 11.6 Future Directions. ; 11.7 Conclusions.
• Sommario/riassunto: Handbook of Forensic Statistics is a collection of chapters by leading authorities in forensic statistics. Written for statisticians, scientists, and legal professionals having a broad range of statistical expertise, it summarizes and compares basic methods of statistical inference (frequentist, likelihoodist, and Bayesian) for trace and other evidence that links individuals to crimes, the modern history and key controversies in the field, and the psychological and legal aspects of such scientific evidence. Specific topics include uncertainty in measurements and conclusions; statistically valid statements of weight of evidence or source conclusions; admissibility and presentation of statistical findings; and the state of the art of methods (including problems and pitfalls) for collecting, analyzing, and interpreting data in such areas as forensic biology, chemistry, and pattern and impression evidence. The particular types of evidence that are discussed include DNA, latent fingerprints, firearms and toolmarks, glass, handwriting, shoeprints, and voice exemplars.
• Titolo autorizzato: Handbook of forensic statistics
• ISBN: 1-000-09606-8; 0-367-52770-7; 1-000-09620-3
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910811708003321
• Lo trovi qui: Univ. Federico II