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Book
xi, 256 pages : illustrations, portraits ; 25 cm
  • Introduction
  • Math error number 1 : multiplying non-independent probabilities : the case of Sally Clark : motherhood under attack
  • Math error number 2 : unjustified estimates : the case of Janet Collins : hairstyle probability
  • Math error number 3 : trying to get something from nothing : the case of Joe Sneed : absent from the phone book
  • Math error number 4 : double experiment : the case of Meredith Kercher : the test that wasn't done
  • Math error number 5 : the birthday problem : the cold case of Diana Sylvester : cold hit analysis
  • Math error number 6 : Simpson's paradox : the Berkeley sex bias case : discrimination detection
  • Math error number 7 : the incredible coincidence : the case of Lucia de Berk : carer or killer?
  • Math error number 8 : underestimation : the case of Charles Ponzi : American dream, American scheme
  • Math error number 9 : choosing a wrong model : the case of Hetty Green : a battle of wills
  • Math error number 10 : mathematical madness : the Dreyfus affair : spy or scapegoat?.
In the wrong hands, math can be deadly. Even the simplest numbers can become powerful forces when manipulated by politicians or the media, but in the case of the law, your liberty--and your life--can depend on the right calculation. In Math on Trial, mathematicians Leila Schneps and Coralie Colmez describe ten trials spanning from the nineteenth century to today, in which mathematical arguments were used--and disastrously misused--as evidence. They tell the stories of Sally Clark, who was accused of murdering her children by a doctor with a faulty sense of calculation; of nineteenth-century tycoon Hetty Green, whose dispute over her aunt's will became a signal case in the forensic use of mathematics; and of the case of Amanda Knox, in which a judge's misunderstanding of probability led him to discount critical evidence--which might have kept her in jail. Offering a fresh angle on cases from the nineteenth-century Dreyfus affair to the murder trial of Dutch nurse Lucia de Berk, Schneps and Colmez show how the improper application of mathematical concepts can mean the difference between walking free and life in prison. A colorful narrative of mathematical abuse, Math on Trial blends courtroom drama, history, and math to show that legal expertise isn't always enough to prove a person innocent.
(source: Nielsen Book Data)9780465032921 20160615
Law Library (Crown)
Book
xviii, 276 p. : ill. ; 25 cm.
  • Preface Interpreting Case Citations PART I: SAMPLES AND POPULATIONS Samples and Populations Audits Determining the Appropriate Population Representative Samples and Jury Selection Concepts Issues Composition of the Jury Pool Random Selection To Learn More Sample and Survey Methodology Concepts Sampling Methodology Increasing Sample Reliability Missing Data and Nonresponders Presenting Your Case Concepts The Center or Average Measures of Precision Changes in Rates PART II: PROBABILITY Probability Concepts Equally-Likely, Equally Probable, Equally Frequent Mutually-Exclusive Events Conditional Probabilities Independence Bayes Theorem To Learn More Criminal Law Facts not Probabilities Observation vs Guesstimates Probable Cause Sentencing To Learn More Civil Law The Civil Paradigm Holdings Speculative Gains and Losses To Learn More Environmental Hazards Concepts Is the Evidence Admissible? Is the Evidence Sufficient? Risk v. Probability Use of Models Observations v. Experiments Multiple Defendants PART III: HYPOTHESIS TESTING AND ESTIMATION How Large is Large? Discrimination 80% Rule No Sample Too Small Methods of Analysis Comparing Two Samples The Underlying Population Distribution Theory Contingency Tables To Learn More Correlation Correlation Bias Testing Linear Regression Sidebar: Limitations of Regression Multivariate Regression Lost Earnings Multiple Applications Collinearity and Partial Correlation Defenses Rebuttal Decisions Alternate Forms of Regression Analysis When Statistics Don't Count Counter-Attack PART IV: APPLYING STATISTICS IN THE COURTROOM Preventative Statistics Concepts Appropriate Controls Power of a Test Coincidence Recognizing Bad Statistics The Trial Process-For the Statistician Selecting the Case Pre-Filing Discovery Depositions Post-Deposition, Pre-Trial In the Courtroom Appeals Making Effective Use of Statistics and Statisticians-For the Attorney Selecting the Statistician Pre-Filing Preparation Discovery Presentation of Evidence Appeals TABLE OF AUTHORITIES BIBLIOGRAPHY INDEX.
  • (source: Nielsen Book Data)9781584882718 20160528
This publication is directed at both the attorney and the statistician to ensure they will successfully apply statistics in the law. The attorney will learn how best to utilize statistics while gaining an enriched understanding of the law on audits, jury selection, discrimination, environmental hazards, evidence, and torts as it relates to statistical issues. Statisticians will learn that the law is what judges say it is and to frame their arguments accordingly. Applying Statistics in the Courtroom: A New Approach for Attorneys and Expert Witnesses will increase the effectiveness of both the attorney and the statistician in presenting and attacking statistical arguments in the courtroom.
(source: Nielsen Book Data)9781584882718 20160528
Law Library (Crown)
Book
xviii, 276 p. : ill.
  • Preface Interpreting Case Citations PART I: SAMPLES AND POPULATIONS Samples and Populations Audits Determining the Appropriate Population Representative Samples and Jury Selection Concepts Issues Composition of the Jury Pool Random Selection To Learn More Sample and Survey Methodology Concepts Sampling Methodology Increasing Sample Reliability Missing Data and Nonresponders Presenting Your Case Concepts The Center or Average Measures of Precision Changes in Rates PART II: PROBABILITY Probability Concepts Equally-Likely, Equally Probable, Equally Frequent Mutually-Exclusive Events Conditional Probabilities Independence Bayes Theorem To Learn More Criminal Law Facts not Probabilities Observation vs Guesstimates Probable Cause Sentencing To Learn More Civil Law The Civil Paradigm Holdings Speculative Gains and Losses To Learn More Environmental Hazards Concepts Is the Evidence Admissible? Is the Evidence Sufficient? Risk v. Probability Use of Models Observations v. Experiments Multiple Defendants PART III: HYPOTHESIS TESTING AND ESTIMATION How Large is Large? Discrimination 80% Rule No Sample Too Small Methods of Analysis Comparing Two Samples The Underlying Population Distribution Theory Contingency Tables To Learn More Correlation Correlation Bias Testing Linear Regression Sidebar: Limitations of Regression Multivariate Regression Lost Earnings Multiple Applications Collinearity and Partial Correlation Defenses Rebuttal Decisions Alternate Forms of Regression Analysis When Statistics Don't Count Counter-Attack PART IV: APPLYING STATISTICS IN THE COURTROOM Preventative Statistics Concepts Appropriate Controls Power of a Test Coincidence Recognizing Bad Statistics The Trial Process-For the Statistician Selecting the Case Pre-Filing Discovery Depositions Post-Deposition, Pre-Trial In the Courtroom Appeals Making Effective Use of Statistics and Statisticians-For the Attorney Selecting the Statistician Pre-Filing Preparation Discovery Presentation of Evidence Appeals TABLE OF AUTHORITIES BIBLIOGRAPHY INDEX.
  • (source: Nielsen Book Data)9781584882718 20160528
This publication is directed at both the attorney and the statistician to ensure they will successfully apply statistics in the law. The attorney will learn how best to utilize statistics while gaining an enriched understanding of the law on audits, jury selection, discrimination, environmental hazards, evidence, and torts as it relates to statistical issues. Statisticians will learn that the law is what judges say it is and to frame their arguments accordingly. Applying Statistics in the Courtroom: A New Approach for Attorneys and Expert Witnesses will increase the effectiveness of both the attorney and the statistician in presenting and attacking statistical arguments in the courtroom.
(source: Nielsen Book Data)9781584882718 20160528
Book
xvii, 357 p. ; 25 cm.
Law Library (Crown)
Book
xxii, 443 p. : ill. ; 24 cm.
  • Evidence Interpretation and Sample Size Determination, C.G.G. Aitken.- Statistical Issues in the Application of the Federal Sentencing Guidelines in Drug, Pornography, and Fraud Cases, Alan J. Izenman.- Interpreting DNA Evidence: Can Probability Theory Help? David J. Balding.- Statistics, Litigation, and Conduct Unbecoming, Seymour Geisser.- The Consequences of Defending DNA Statistics, Bruce S. Weir.- DNA Statistics Under Trial in the Australia Adversarial System, Janet Chaseling.- A Likelihood Approach to DNA Evidence, Beverly Mellen.- The Choice of Hypotheses When Evaluating DNA Profile Evidence, Anders Stockmarr.- On the Evolution of Analytical Proof, Statistics and the Use of Experts in EEO Litigation, Marc Rosenblum.- A Connecticut Jury Array Challenge, David Pollard.- Issues Arising in the Use of Statistical Evidence in Discrimination Cases, Joseph L. Gastwirth.- Statistical Consulting in the Legal Environment, Charles R. Mann.- Epidemiological Causation in the Legal Context: Substance and Procedures, Sana Loue.- Judicial Review of Statistical Analyses in Environmental Rulemakings, Wendy E. Wagner.- Assessing Costs of Smoking for Minnesota - vs. - Tobacco-- The Perspective of Statistical Experts in a Landmark Civil Case, Scott L. Zeger, Timothy Wyant, Leonard Miller, and Jonathan Samet.- Statistical Issues in the Estimation of the Causal Effects of Smoking Due to the Conduct of the Tobacco Industry, Donald B. Rubin.- Forensic Statistics and Multiparty Bayesianism, Joseph B. Kadane.- Warranty Contracts and Equilibrium Probabilities, Nozer D. Singpurwalla.- Death and Deterrence: Notes on a Still Inchoate Judicial Inquiry, Robert J. Cottrol.- Introduction to Two Views on the Shonubi Case, Alan J. Izenman.- Assessing the Statistical Evidence in the Shonubi Case, Alan Izenman.- The Shonubi Case as Example of the Legal System's Failure to Appreciate Statistical Evidence, Joseph L. Gastwirth, Boris Freidlin, and Weiwen.- Assessing the Statistical Evidence in the Shonubi Case, Alan J. Izenman.
  • (source: Nielsen Book Data)9780387989976 20160528
Expert testimony relying on scientific and other specialized evidence has come under increased scrutiny by the legal system. A trilogy of recent U.S. Supreme Court cases has assigned judges the task of assessing the relevance and reliability of proposed expert testimony. In conjunction with the Federal judiciary, the American Association for the Advancement of Science has initiated a project to provide judges indicating a need with their own expert. This concern with the proper interpretation of scientific evidence, especially that of a probabilistic nature, has also occurred in England, Australia and in several European countries."Statistical Science in the Courtroom" is a collection of articles written by statisticians and legal scholars who have been concerned with problems arising in the use of statistical evidence. A number of articles describe DNA evidence and the difficulties of properly calculating the probability that a random individual's profile would "match" that of the evidence as well as the proper way to interpret the result. In addition to the technical issues, several authors tell about their experiences in court. A few have become disenchanted with their involvement and describe the events that led them to devote less time to this application. Other articles describe the role of statistical evidence in cases concerning discrimination against minorities, product liability, environmental regulation, the appropriateness and fairness of sentences and how being involved in legal statistics has raised interesting statistical problems requiring further research.
(source: Nielsen Book Data)9780387989976 20160528
Law Library (Crown)
Book
1 online resource (xxii, 443 pages) : illustrations.
  • Evidence Interpretation and Sample Size Determination, C.G.G. Aitken.- Statistical Issues in the Application of the Federal Sentencing Guidelines in Drug, Pornography, and Fraud Cases, Alan J. Izenman.- Interpreting DNA Evidence: Can Probability Theory Help? David J. Balding.- Statistics, Litigation, and Conduct Unbecoming, Seymour Geisser.- The Consequences of Defending DNA Statistics, Bruce S. Weir.- DNA Statistics Under Trial in the Australia Adversarial System, Janet Chaseling.- A Likelihood Approach to DNA Evidence, Beverly Mellen.- The Choice of Hypotheses When Evaluating DNA Profile Evidence, Anders Stockmarr.- On the Evolution of Analytical Proof, Statistics and the Use of Experts in EEO Litigation, Marc Rosenblum.- A Connecticut Jury Array Challenge, David Pollard.- Issues Arising in the Use of Statistical Evidence in Discrimination Cases, Joseph L. Gastwirth.- Statistical Consulting in the Legal Environment, Charles R. Mann.- Epidemiological Causation in the Legal Context: Substance and Procedures, Sana Loue.- Judicial Review of Statistical Analyses in Environmental Rulemakings, Wendy E. Wagner.- Assessing Costs of Smoking for Minnesota - vs. - Tobacco-- The Perspective of Statistical Experts in a Landmark Civil Case, Scott L. Zeger, Timothy Wyant, Leonard Miller, and Jonathan Samet.- Statistical Issues in the Estimation of the Causal Effects of Smoking Due to the Conduct of the Tobacco Industry, Donald B. Rubin.- Forensic Statistics and Multiparty Bayesianism, Joseph B. Kadane.- Warranty Contracts and Equilibrium Probabilities, Nozer D. Singpurwalla.- Death and Deterrence: Notes on a Still Inchoate Judicial Inquiry, Robert J. Cottrol.- Introduction to Two Views on the Shonubi Case, Alan J. Izenman.- Assessing the Statistical Evidence in the Shonubi Case, Alan Izenman.- The Shonubi Case as Example of the Legal System's Failure to Appreciate Statistical Evidence, Joseph L. Gastwirth, Boris Freidlin, and Weiwen.- Assessing the Statistical Evidence in the Shonubi Case, Alan J. Izenman.
  • (source: Nielsen Book Data)9780387989976 20160528
Expert testimony relying on scientific and other specialized evidence has come under increased scrutiny by the legal system. A trilogy of recent U.S. Supreme Court cases has assigned judges the task of assessing the relevance and reliability of proposed expert testimony. In conjunction with the Federal judiciary, the American Association for the Advancement of Science has initiated a project to provide judges indicating a need with their own expert. This concern with the proper interpretation of scientific evidence, especially that of a probabilistic nature, has also occurred in England, Australia and in several European countries."Statistical Science in the Courtroom" is a collection of articles written by statisticians and legal scholars who have been concerned with problems arising in the use of statistical evidence. A number of articles describe DNA evidence and the difficulties of properly calculating the probability that a random individual's profile would "match" that of the evidence as well as the proper way to interpret the result. In addition to the technical issues, several authors tell about their experiences in court. A few have become disenchanted with their involvement and describe the events that led them to devote less time to this application. Other articles describe the role of statistical evidence in cases concerning discrimination against minorities, product liability, environmental regulation, the appropriateness and fairness of sentences and how being involved in legal statistics has raised interesting statistical problems requiring further research.
(source: Nielsen Book Data)9780387989976 20160528
Book
2 v. : ill. ; 24 cm.
  • v. 1. Statistical concepts and issues of fairness.
  • v. 2. Tort law, evidence, and health.
Law Library (Crown)
Book
xxxv, 651 p. : ill. ; 24 cm.
Law Library (Crown)
Book
viii, 211 p. : ill. ; 24 cm.
Many legal disputes turn on some form of the question, Why did they do that? Using examples involving employment discrimination, political redistricting, jury selection and computer code theft, we demonstrate that a novel analytical framework connects these diverse cases. When this framework is applied to pay discrimination cases, it yields information that is more relevant to the issues in dispute than does the traditional framework.
(source: Nielsen Book Data)9781430306535 20160528
Law Library (Crown)
Book
1 online resource (338 pages) : illustrations
  • 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.
  • (source: Nielsen Book Data)9780470972106 20160711
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.
(source: Nielsen Book Data)9780470972106 20160711
Book
1 online resource (1 volume) : illustrations
  • 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.
  • (source: Nielsen Book Data)9781118763155 20160711
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.
(source: Nielsen Book Data)9781118763155 20160711
Book
1 online resource ()
  • Preface xiii 1 Physicochemical data obtained in forensic science laboratories 1 1.1 Introduction 1 1.2 Glass 2 1.2.1 SEM-EDX technique 4 1.2.2 GRIM technique 5 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 2.2.1 Two-stage approach 21 2.2.2 Likelihood ratio approach 23 2.2.3 Difference between an application of two-stage approach and likelihood ratio approach 26 2.3 Classification problem 27 2.3.1 Chemometric approach 27 2.3.2 Likelihood ratio approach 31 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 3.3.1 Measures of location 39 3.3.2 Dispersion: Variance estimation 42 3.3.3 Data distribution 44 3.3.4 Correlation 45 3.3.5 Continuous probability distributions 49 3.4 Hypothesis testing 59 3.4.1 Introduction 59 3.4.2 Hypothesis test for a population mean for samples with known variance sigma2 from a normal distribution 60 3.4.3 Hypothesis test for a population mean for small samples with unknown variance sigma2 from a normal distribution 63 3.4.4 Relation between tests and confidence intervals 67 3.4.5 Hypothesis test based on small samples for a difference in the means of two independent populations with unknown variances from normal distributions 68 3.4.6 Paired comparisons 72 3.4.7 Hotelling's T 2 test 75 3.4.8 Significance test for correlation coefficient 77 3.5 Analysis of variance 78 3.5.1 Principles of ANOVA 78 3.5.2 Feature selection with application of ANOVA 82 3.5.3 Testing of the equality of variances 85 3.6 Cluster analysis 85 3.6.1 Similarity measurements 86 3.6.2 Hierarchical cluster analysis 89 3.7 Dimensionality reduction 92 3.7.1 Principal component analysis 93 3.7.2 Graphical models 99 References 105 4 Likelihood ratio models for comparison problems 107 4.1 Introduction 107 4.2 Normal between-object distribution 108 4.2.1 Multivariate data 109 4.2.2 Univariate data 110 4.3 Between-object distribution modelled by kernel density estimation 110 4.3.1 Multivariate data 111 4.3.2 Univariate data 111 4.4 Examples 112 4.4.1 Univariate research data -- normal between-object distribution -- R software 112 4.4.2 Univariate casework data -- normal between-object distribution -- Bayesian network 116 4.4.3 Univariate research data -- kernel density estimation -- R software 119 4.4.4 Univariate casework data -- kernel density estimation -- calcuLatoR software 125 4.4.5 Multivariate research data -- normal between-object distribution -- R software 127 4.4.6 Multivariate research data -- kernel density estimation procedure -- R software 129 4.4.7 Multivariate casework data -- kernel density estimation -- R software 137 4.5 R Software 140 4.5.1 Routines for casework applications 140 4.5.2 Routines for research applications 144 References 149 5 Likelihood ratio models for classification problems 151 5.1 Introduction 151 5.2 Normal between-object distribution 152 5.2.1 Multivariate data 153 5.2.2 Univariate data 153 5.2.3 One-level models 154 5.3 Between-object distribution modelled by kernel density estimation 155 5.3.1 Multivariate data 155 5.3.2 Univariate data 156 5.3.3 One-level models 156 5.4 Examples 157 5.4.1 Univariate casework data -- normal between-object distribution -- Bayesian network 158 5.4.2 Univariate research data -- kernel density estimation procedure -- R software 161 5.4.3 Multivariate research data -- kernel density estimation -- R software 164 5.4.4 Multivariate casework data -- kernel density estimation -- R software 169 5.5 R software 172 5.5.1 Routines for casework applications 172 5.5.2 Routines for research applications 175 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 6.4.1 False positive and false negative rates 187 6.4.2 Discriminating power: A definition 188 6.4.3 Measuring discriminating power with DET curves 190 6.4.4 Is discriminating power enough? 192 6.5 Accuracy equals discriminating power plus calibration: Empirical cross-entropy plots 192 6.5.1 Accuracy in a classical example: Weather forecasting 193 6.5.2 Calibration 195 6.5.3 Adaptation to forensic inference using likelihood ratios 196 6.6 Comparison of the performance of different methods for LR computation 200 6.6.1 MSP-DAD data from comparison of inks 200 6.6.2 Py-GC/MS data from comparison of car paints 205 6.6.3 SEM-EDX data for classification of glass objects 209 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.2.1 Vector algebra 248 D.2.2 Matrix algebra 250 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.7.1 Box-plots 258 D.7.2 Q-Q plots 259 D.7.3 Normal distribution 260 D.7.4 Histograms 262 D.7.5 Kernel density estimation 263 D.7.6 Correlation between variables 263 D.8 Saving data 266 D.9 R codes used in Chapters 4 and 5 266 D.9.1 Comparison problems in casework studies 266 D.9.2 Comparison problems in research studies 273 D.9.3 Classification problems in casework studies 278 D.9.4 Classification problems in research studies 285 D.10 Evaluating the performance of LR models 289 D.10.1 Histograms 289 D.10.2 Tippett plots 290 D.10.3 DET plots 291 D.10.4 ECE plots 292 Reference 293 Appendix E Bayesian network models 294 E.1 Introduction to Bayesian networks 294 E.2 Introduction to Hugin ResearcherTM software 296 E.2.1 Basic functions 297 E.2.2 Creating a new Bayesian network 298 E.2.3 Calculations 302 References 308 Appendix F Introduction to calcuLatoR software 309 F.1 Introduction 309 F.2 Manual 309 Reference 314 Index 315.
  • (source: Nielsen Book Data)9781118763155 20160612
  • Preface xiii 1 Physicochemical data obtained in forensic sciencelaboratories 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 data19 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 densityestimation 110 4.4 Examples 112 4.5 R Software 140 References 149 5 Likelihood ratio models for classification problems151 5.1 Introduction 151 5.2 Normal between-object distribution 152 5.3 Between-object distribution modelled by kernel densityestimation 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 likelihoodratios 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 LRcomputation 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 algebra228 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.
  • (source: Nielsen Book Data)9780470972106 20160612
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.
(source: Nielsen Book Data)9781118763155 20160612
A practical guide for determining the evidential value ofphysicochemical data Microtraces of various materials (e.g. glass, paint, fibres, andpetroleum products) are routinely subjected to physicochemicalexamination by forensic experts, whose role is to evaluate suchphysicochemical data in the context of the prosecution and defencepropositions. Such examinations return various kinds ofinformation, including quantitative data. From the forensic pointof view, the most suitable way to evaluate evidence is thelikelihood ratio. This book provides a collection of recentapproaches to the determination of likelihood ratios and describessuitable software, with documentation and examples of their use inpractice. The statistical computing and graphics softwareenvironment R, pre-computed Bayesian networks using HuginResearcher and a new package, calcuLatoR, for thecomputation of likelihood ratios are all explored. Statistical Analysis in Forensic Science will provide aninvaluable practical guide for forensic experts and practitioners, forensic statisticians, analytical chemists, andchemometricians. Key features include: * Description of the physicochemical analysis of forensic traceevidence. * Detailed description of likelihood ratio models for determiningthe evidential value of multivariate physicochemicaldata. * Detailed description of methods, such as empiricalcross-entropy plots, for assessing the performance of likelihoodratio-based methods for evidence evaluation. * Routines written using the open-source R software, aswell as Hugin Researcher and calcuLatoR. * Practical examples and recommendations for the use of all thesemethods in practice.
(source: Nielsen Book Data)9780470972106 20160612
Book
1 online resource (84 pages) : color illustrations Digital: text file.
Collection
Government Information United States Federal Collection
"This report describes the results from a National Institute of Justice funded statistical research project through the National Center of Forensic Science at the University of Central Florida. The motivation of the study was to strengthen the statistical basis for handwriting comparisons, following the recognition that the discipline of forensic document examination was facing increasing judicial scrutiny under the Daubert guidelines as recognized by the profession and subsequently reported in the National Research Council report, Strengthening Forensic Science in the United States: A Path Forward (2009). In response, this project's objectives were to develop statistically valid frequency occurrence proportions for selected characteristics of handwriting and hand printing based on specimen samples representative of the United States population, to provide practitioners of forensic document examination with a statistical basis for reliability and measurement validity and to provide courts with the requested supporting data The project produced an initial set of over 2500 precise handwriting and hand printing features that were subsequently reduced to 903 features which passed an attribute agreement analysis and to 786 that were utilized in this project. These attribute features (presence/absence) can be unambiguously identified by forensic document examiners. Handwriting samples from over 1500 writers were collected representing a broad spectrum of contributors intended to be representative of the US adult population. Meeting the pre-specified population representation led to the selection of a subset of 880 cursive specimens and 839 hand printed specimens that closely approximated the demographic proportions represented in the US. The analysis of these specimens yielded numerous specific frequency occurrence proportions. Additional analyses have shown quantitatively the extent to which demographic features such as age, gender, ethnicity, education, location of second/third grade training and handedness impact the presence/absence of features. An immediate benefit of the databases analysis has been a detailed assessment of the scope of the appropriateness of the product rule. This project relied heavily on international standards and appropriate statistical methodology to develop the sampling protocols."--Abstract.
Book
242 p. ; 25 cm.
  • Probability-- populations and samples-- weight of evidence and the Bayesian likelihood ratio-- transfer evidence-- application of statistics to particular areas of forensic science-- knowledge-based systems-- quality- based systems.
  • (source: Nielsen Book Data)9780139337482 20160528
Describes ways of assessing forensic science evidence and the means of communicating the assessment to a court of law. The aim of this work is to ensure that the courts consider seriously the probability of the evidence of association. Aitken; C. G. G. University of Endinburgh, Scotland, Stoney; David A. University of Illinois at Chicago, Oak Park, Illinois, USA, .
(source: Nielsen Book Data)9780139337482 20160528
Law Library (Crown)
Book
242 p. : ill.
  • Probability-- populations and samples-- weight of evidence and the Bayesian likelihood ratio-- transfer evidence-- application of statistics to particular areas of forensic science-- knowledge-based systems-- quality- based systems.
  • (source: Nielsen Book Data)9780139337482 20160528
Describes ways of assessing forensic science evidence and the means of communicating the assessment to a court of law. The aim of this work is to ensure that the courts consider seriously the probability of the evidence of association. Aitken; C. G. G. University of Endinburgh, Scotland, Stoney; David A. University of Illinois at Chicago, Oak Park, Illinois, USA, .
(source: Nielsen Book Data)9780139337482 20160528
Book
2 volumes ; 23 cm
  • Volume 46, number 1. Symposium on forensic statistics : a foreword / D. H. Kaye
  • The importance of checking the assumptions underlying statistical analysis : graphical methods for assessing normality / Yulia Gel, Weiwen Miao, Joseph L. Gastwirth
  • Assessing spatial heterogeneity in the refractive index of float glass / Geva Maimon, Russell J. Steele, James M. Curran
  • An introduction to data fusion, data mining, and pattern recognition applied to fiber analysis / Jennifer Wiseman Mercer, Suzanne C. Bell
  • Analyzing the relevance and admissibility of bullet-lead evidence : did the NRC report miss the target / William C. Thompson
  • The NRC bullet-lead report : should science committees make legal findings / D. H. Kaye
  • To tell the truth : on the probative value of polygraph search evidence / Stephen E. Fienberg.
  • Volume 46, number 2. "Implicit testing" : can casework validate forensic techniques? / Simon A. Cole
  • Statistical assessment of damages in breach of contract litigation / Duane L. Steffey, Stephen E. Fienberg, Robert H. Sturgess
  • Articles. Power-law distributions and the federal judiciary / Thomas Bak
  • Assessing the implications for close relatives in the event of similar but nonmatching DNA profiles / David R. Paoletti, Travis E. Doom, Michael L. Raymer, Dan E. Krane
  • Beyond the ken? : testing jurors' understanding of eyewitness reliability evidence / Richard S. Schmechel, Timothy P. O'Toole, Catharine Easterly, Elizabeth F. Loftus.
Law Library (Crown)
Book
xxi, 649 p. : ill.
  • Benford's Law-- Forensic Digital Analysis & Fraud Detection-- Data Compliance Tests-- Conceptual and Mathematical Foundations-- Benford's Law in the Physical Sciences-- Topics in Benford's Law-- The Law of Relative Quantities.
  • (source: Nielsen Book Data)9789814583688 20160617
This book tells the story of a newly discovered and apparently mysterious digital phenomenon of Benford's Law. This phenomenon manifests itself by the empirical finding that not all digits are created equal, but rather that low digits such as 1, 2, 3 occur much more frequently than high digits such as 7, 8, 9, in accounting, financial, scientific, and almost all other data types. This work represents the first ever published work giving a comprehensive and in depth account of all the theoretical aspects and applications of Benford's Law. The reader is subsequently led into a fascinating intellectual journey through the interacting worlds of digits, numbers, and quantities, a journey that ends with the compelling conclusion that the entire phenomenon is truly quantitative in nature, and applicable just as well to the ancient Roman, Mayan, and Egyptian digit-less civilizations. The second section covers the applications of the law in forensic data analysis for the purpose of fraud detection. It is concise, reader-friendly, and can be understood without deep knowledge in statistical theory or difficult mathematics. This fraud detection section gathers all known methods, results, and standards in the accounting and auditing industry, from quite a wide variety of articles on this issue, summarizes and fuses them into a singular coherent whole. In addition, a newly invented (patent-pending) digital algorithm is presented, enabling the auditor to detect such fraud even when the sophisticated and well-educated cheater is aware of the law and attempts to appear as if he or she is innocently complying with the digital pattern. A large portion of the book is devoted to understanding the variety of causes explanations of the phenomenon. Seeing Benford's Law in this bird's eye view enables the reader to see the forest in all its glory and beauty instead of tiring one's self repeatedly checking individual trees.
(source: Nielsen Book Data)9789814583688 20160617
Book
xxx, 509 p. : ill. ; 24 cm.
  • Uncertainty in forensic science
  • Variation
  • The evaluation of evidence
  • Historical review
  • Bayesian inference
  • Sampling
  • Interpretation
  • Transfer evidence
  • Discrete data
  • Continuous data
  • Multivariate analysis
  • Fibres
  • DNA profiling
  • Bayesian networks.
dx.doi.org Wiley Online Library
Book
331 p.
  • Introduction Who is this book for? What this book is not about How to read this book How this book was written Why R? Basic statistics Who should read this chapter? Introduction Definitions Simple descriptive statistics Summarizing data Installing R on your computer Reading data into R The dafs package R tutorial Graphics Who should read this chapter? Introduction Why are we doing this? Flexible versus \canned" Drawing simple graphs Annotating and embellishing plots R graphics tutorial Further reading Hypothesis tests and sampling theory Who should read this chapter? Topics covered in this chapter Additional reading Statistical distributions Introduction to statistical hypothesis testing Tutorial The linear model Who should read this? How to read this chapter Simple linear regression Multiple linear regression Calibration in the simple linear regression case Regression with factors Linear models for grouped data - One way ANOVA Two way ANOVA Unifying the linear model Modeling count and proportion data Who should read this? How to read this chapter Introduction to GLMs Poisson regression or Poisson GLMs The negative binomial GLM Logistic regression or the binomial GLM Deviance The design of experiments Introduction Who should read this chapter? What is an experiment? The components of an experiment The principles of experimental design The description and analysis of experiments Fixed and random effects Completely randomized designs Randomized complete block designs Designs with fewer experimental units Further reading.
  • (source: Nielsen Book Data)9781420088274 20160605
Statistical methods provide a logical, coherent framework in which data from experimental science can be analyzed. However, many researchers lack the statistical skills or resources that would allow them to explore their data to its full potential. Introduction to Data Analysis with R for Forensic Sciences minimizes theory and mathematics and focuses on the application and practice of statistics to provide researchers with the dexterity necessary to systematically analyze data discovered from the fruits of their research. Using traditional techniques and employing examples and tutorials with real data collected from experiments, this book presents the following critical information necessary for researchers: * A refresher on basic statistics and an introduction to R * Considerations and techniques for the visual display of data through graphics * An overview of statistical hypothesis tests and the reasoning behind them * A comprehensive guide to the use of the linear model, the foundation of most statistics encountered * An introduction to extensions to the linear model for commonly encountered scenarios, including logistic and Poisson regression * Instruction on how to plan and design experiments in a way that minimizes cost and maximizes the chances of finding differences that may exist Focusing on forensic examples but useful for anyone working in a laboratory, this volume enables researchers to get the most out of their experiments by allowing them to cogently analyze the data they have collected, saving valuable time and effort.
(source: Nielsen Book Data)9781420088274 20160605
Book
xv, 436 p. : ill. ; 26 cm.
  • Introducing numbers to the legal profession
  • Math basics
  • Logic and set theory
  • Probability theory
  • Statistics
  • Graphs and diagrams
  • Classical physics
  • Modern physics
  • Accounting and finance.
Law Library (Crown)

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