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020    9781118763179|q(electronic bk.) 
020    |z9780470972106 
035    (MiAaPQ)EBC1557286 
035    (Au-PeEL)EBL1557286 
035    (CaPaEBR)ebr10804687 
035    (CaONFJC)MIL543110 
035    (OCoLC)858778356 
040    MiAaPQ|beng|erda|epn|cMiAaPQ|dMiAaPQ 
050  4 RA1057.S73 2014eb 
082 0  363.25015195 
100 1  Zadora, Grzegorz 
245 10 Statistical Analysis in Forensic Science :|bEvidential 
       Value of Multivariate Physicochemical Data 
250    1st ed 
264  1 New York :|bJohn Wiley & Sons, Incorporated,|c2014 
264  4 |c©2013 
300    1 online resource (338 pages) 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
505 0  Intro -- Statistical Analysis in Forensic Science -- 
       Contents -- Preface -- 1 Physicochemical data obtained in 
       forensic science laboratories -- 1.1 Introduction -- 1.2 
       Glass -- 1.2.1 SEM-EDX technique -- 1.2.2 GRIM technique -
       - 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 -- 2.2.1 Two-stage approach -- 2.2.2 
       Likelihood ratio approach -- 2.2.3 Difference between an 
       application of two-stage approach and likelihood ratio 
       approach -- 2.3 Classification problem -- 2.3.1 
       Chemometric approach -- 2.3.2 Likelihood ratio approach --
       2.4 Likelihood ratio and Bayes' theorem -- References -- 3
       Continuous data -- 3.1 Introduction -- 3.2 Data 
       transformations -- 3.3 Descriptive statistics -- 3.3.1 
       Measures of location -- 3.3.2 Dispersion: Variance 
       estimation -- 3.3.3 Data distribution -- 3.3.4 Correlation
       -- 3.3.5 Continuous probability distributions -- 3.4 
       Hypothesis testing -- 3.4.1 Introduction -- 3.4.2 
       Hypothesis test for a population mean for samples with 
       known variance  from a normal distribution -- 3.4.3 
       Hypothesis test for a population mean for small samples 
       with unknown variance  from a normal distribution -- 3.4.4
       Relation between tests and confidence intervals -- 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 -- 3.4.6 Paired 
       comparisons -- 3.4.7 Hotelling's test -- 3.4.8 
       Significance test for correlation coefficient -- 3.5 
       Analysis of variance -- 3.5.1 Principles of ANOVA -- 3.5.2
       Feature selection with application of ANOVA -- 3.5.3 
       Testing of the equality of variances -- 3.6 Cluster 
       analysis -- 3.6.1 Similarity measurements 
505 8  3.6.2 Hierarchical cluster analysis -- 3.7 Dimensionality 
       reduction -- 3.7.1 Principal component analysis -- 3.7.2 
       Graphical models -- References -- 4 Likelihood ratio 
       models for comparison problems -- 4.1 Introduction -- 4.2 
       Normal between-object distribution -- 4.2.1 Multivariate 
       data -- 4.2.2 Univariate data -- 4.3 Between-object 
       distribution modelled by kernel density estimation -- 
       4.3.1 Multivariate data -- 4.3.2 Univariate data -- 4.4 
       Examples -- 4.4.1 Univariate research data - normal 
       between-object distribution - R software -- 4.4.2 
       Univariate casework data - normal between-object 
       distribution - Bayesian network -- 4.4.3 Univariate 
       research data - kernel density estimation - R software -- 
       4.4.4 Univariate casework data - kernel density estimation
       - calcuLatoR software -- 4.4.5 Multivariate research data 
       - normal between-object distribution - R software -- 4.4.6
       Multivariate research data - kernel density estimation 
       procedure - R software -- 4.4.7 Multivariate casework data
       - kernel density estimation - R software -- 4.5 R Software
       -- 4.5.1 Routines for casework applications -- 4.5.2 
       Routines for research applications -- References -- 5 
       Likelihood ratio models for classification problems -- 5.1
       Introduction -- 5.2 Normal between-object distribution -- 
       5.2.1 Multivariate data -- 5.2.2 Univariate data -- 5.2.3 
       One-level models -- 5.3 Between-object distribution 
       modelled by kernel density estimation -- 5.3.1 
       Multivariate data -- 5.3.2 Univariate data -- 5.3.3 One-
       level models -- 5.4 Examples -- 5.4.1 Univariate casework 
       data - normal between-object distribution - Bayesian 
       network -- 5.4.2 Univariate research data - kernel density
       estimation procedure - R software -- 5.4.3 Multivariate 
       research data - kernel density estimation - R software -- 
       5.4.4 Multivariate casework data - kernel density 
       estimation - R software -- 5.5 R software 
505 8  5.5.1 Routines for casework applications -- 5.5.2 Routines
       for research applications -- 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 -- 6.4.1 False positive and
       false negative rates -- 6.4.2 Discriminating power: A 
       definition -- 6.4.3 Measuring discriminating power with 
       DET curves -- 6.4.4 Is discriminating power enough? -- 6.5
       Accuracy equals discriminating power plus calibration: 
       Empirical cross-entropy plots -- 6.5.1 Accuracy in a 
       classical example: Weather forecasting -- 6.5.2 
       Calibration -- 6.5.3 Adaptation to forensic inference 
       using likelihood ratios -- 6.6 Comparison of the 
       performance of different methods for LR computation -- 
       6.6.1 MSP-DAD data from comparison of inks -- 6.6.2 Py-GC/
       MS data from comparison of car paints -- 6.6.3 SEM-EDX 
       data for classification of glass objects -- 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 
       tomatrix 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.2.1 Vector algebra -- D.2.2 Matrix algebra -- D.3 
       Data input -- D.4 Functions in R -- D.5 Dereferencing 
505 8  D.6 Basic statistical functions -- D.7 Graphics with R -- 
       D.7.1 Box-plots -- D.7.2 Q-Q plots -- D.7.3 Normal 
       distribution -- D.7.4 Histograms -- D.7.5 Kernel density 
       estimation -- D.7.6 Correlation between variables -- D.8 
       Saving data -- D.9 R codes used in Chapters 4 and 5 -- 
       D.9.1 Comparison problems in casework studies -- D.9.2 
       Comparison problems in research studies -- D.9.3 
       Classification problems in casework studies -- D.9.4 
       Classification problems in research studies -- D.10 
       Evaluating the performance of LR models -- D.10.1 
       Histograms -- D.10.2 Tippett plots -- D.10.3 DET plots -- 
       D.10.4 ECE plots -- Reference -- Appendix E Bayesian 
       network models -- E.1 Introduction to Bayesian networks --
       E.2 Introduction to Hugin Researcher™ software -- E.2.1 
       Basic functions -- E.2.2 Creating a new Bayesian network -
       - E.2.3 Calculations -- References -- Appendix F 
       Introduction to calcuLatoR software -- F.1 Introduction --
       F.2 Manual -- Reference -- Index 
520    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. 
588    Description based on publisher supplied metadata and other
       sources 
590    Electronic reproduction. Ann Arbor, Michigan : ProQuest 
       Ebook Central, 2020. Available via World Wide Web. Access 
       may be limited to ProQuest Ebook Central affiliated 
       libraries 
650  0 Chemistry, Forensic.;Forensic statistics.;Chemometrics 
655  4 Electronic books 
700 1  Martyna, Agnieszka 
700 1  Ramos, Daniel 
700 1  Aitken, Colin 
776 08 |iPrint version:|aZadora, Grzegorz|tStatistical Analysis 
       in Forensic Science : Evidential Value of Multivariate 
       Physicochemical Data|dNew York : John Wiley & Sons, 
       Incorporated,c2014|z9780470972106 
856 40 |uhttps://ebookcentral.proquest.com/lib/sinciatw/
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