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Handbook of forensic statistics / / edited by David L. Banks, Karen Kafadar, David H. Kaye, Maria Tackett
| Handbook of forensic statistics / / edited by David L. Banks, Karen Kafadar, David H. Kaye, Maria Tackett |
| Pubbl/distr/stampa | Boca Raton : , : Chapman & Hall/CRC, , 2021 |
| Descrizione fisica | 1 online resource (571 pages) |
| Disciplina | 363.25015195 |
| Collana | Chapman & Hall/CRC handbooks of modern statistical methods |
| Soggetto topico | Forensic sciences - Statistical methods |
| ISBN |
1-000-09606-8
0-367-52770-7 1-000-09620-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910794481903321 |
| Boca Raton : , : Chapman & Hall/CRC, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Handbook of forensic statistics / / edited by David L. Banks, Karen Kafadar, David H. Kaye, Maria Tackett
| Handbook of forensic statistics / / edited by David L. Banks, Karen Kafadar, David H. Kaye, Maria Tackett |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Boca Raton : , : Chapman & Hall/CRC, , 2021 |
| Descrizione fisica | 1 online resource (571 pages) |
| Disciplina |
363.25015195
519.502436325 |
| Collana | Chapman & Hall/CRC handbooks of modern statistical methods |
| Soggetto topico | Forensic sciences - Statistical methods |
| ISBN |
1-000-09606-8
0-367-52770-7 1-000-09620-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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. |
| Record Nr. | UNINA-9910811708003321 |
| Boca Raton : , : Chapman & Hall/CRC, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||