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Benford's law : theory, the general law of relative quantities, and forensic fraud detection applications / Alex Ely Kossovsky
Benford's law : theory, the general law of relative quantities, and forensic fraud detection applications / Alex Ely Kossovsky
Autore Kossovsky, Alex Ely
Descrizione fisica xxi, 649 p. : ill. ; 24 cm
Disciplina 363.25
Soggetto topico Fraud investigation - Statistical methods
Fraud - Statistical methods
Distribution (Probability theory)
Forensic statistics
Forensic sciences - Statistical methods
ISBN 9789814651202 (paperback)
Classificazione AMS 60-01
AMS 60E05
AMS 11K06
AMS 60F15
LC HV8079.F7K67
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISALENTO-991003364879707536
Kossovsky, Alex Ely  
Materiale a stampa
Lo trovi qui: Univ. del Salento
Opac: Controlla la disponibilità qui
Introduction to data analysis with R for forensic scientists / / James Michael Curran
Introduction to data analysis with R for forensic scientists / / James Michael Curran
Autore Curran James Michael
Pubbl/distr/stampa Boca Raton, Fla. : , : CRC Press, , 2011
Descrizione fisica 1 online resource (324 p.)
Disciplina 363.25/6028552
Collana International forensic science and investigation series
Soggetto topico Forensic sciences - Statistical methods
Forensic statistics
Forensic sciences - Data processing
Criminal investigation - Data processing
R (Computer program language)
Soggetto genere / forma Electronic books.
ISBN 0-429-25021-5
1-4200-8827-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front cover; Dedication; About the author; Acknowledgments; Contents; List of Figures; List of Tables; Chapter 1: Introduction; Chapter 2: Basic statistics; Chapter 3: Graphics; Chapter 4: Hypothesis tests and sampling theory; Chapter 5: The linear model; Chapter 6: Modeling count and proportion data; Chapter 7: The design of experiments; Bibliography; Back cover
Record Nr. UNINA-9910459475803321
Curran James Michael  
Boca Raton, Fla. : , : CRC Press, , 2011
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Introduction to data analysis with R for forensic scientists / / James Michael Curran
Introduction to data analysis with R for forensic scientists / / James Michael Curran
Autore Curran James Michael
Pubbl/distr/stampa Boca Raton, Fla. : , : CRC Press, , 2011
Descrizione fisica 1 online resource (324 p.)
Disciplina 363.25/6028552
Collana International forensic science and investigation series
Soggetto topico Forensic sciences - Statistical methods
Forensic statistics
Forensic sciences - Data processing
Criminal investigation - Data processing
R (Computer program language)
ISBN 1-138-38144-6
0-429-25021-5
1-4200-8827-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front cover; Dedication; About the author; Acknowledgments; Contents; List of Figures; List of Tables; Chapter 1: Introduction; Chapter 2: Basic statistics; Chapter 3: Graphics; Chapter 4: Hypothesis tests and sampling theory; Chapter 5: The linear model; Chapter 6: Modeling count and proportion data; Chapter 7: The design of experiments; Bibliography; Back cover
Record Nr. UNINA-9910785134203321
Curran James Michael  
Boca Raton, Fla. : , : CRC Press, , 2011
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Statistical analysis in forensic science : evidential value of multivariate physicochemical data / / Grzegorz Zadora [and three others]
Statistical analysis in forensic science : evidential value of multivariate physicochemical data / / Grzegorz Zadora [and three others]
Autore Zadora Grzegorz
Edizione [1st edition]
Pubbl/distr/stampa Chichester, West Sussex : , : Wiley, , 2014
Descrizione fisica 1 online resource (338 p.)
Disciplina 614/.12
Altri autori (Persone) ZadoraGrzegorz
MartynaAgnieszka
RamosDaniel
AitkenColin
Soggetto topico Chemistry, Forensic
Forensic statistics
Chemometrics
ISBN 1-118-76318-1
1-118-76315-7
1-118-76317-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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 problem2.3.1 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 distribution3.4.4 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 measurements3.6.2 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 network4.4.3 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
Record Nr. UNINA-9910139027903321
Zadora Grzegorz  
Chichester, West Sussex : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Statistical analysis in forensic science : evidential value of multivariate physicochemical data / / Grzegorz Zadora [and three others]
Statistical analysis in forensic science : evidential value of multivariate physicochemical data / / Grzegorz Zadora [and three others]
Autore Zadora Grzegorz
Edizione [1st edition]
Pubbl/distr/stampa Chichester, West Sussex : , : Wiley, , 2014
Descrizione fisica 1 online resource (338 p.)
Disciplina 614/.12
Altri autori (Persone) ZadoraGrzegorz
MartynaAgnieszka
RamosDaniel
AitkenColin
Soggetto topico Chemistry, Forensic
Forensic statistics
Chemometrics
ISBN 1-118-76318-1
1-118-76315-7
1-118-76317-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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 problem2.3.1 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 distribution3.4.4 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 measurements3.6.2 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 network4.4.3 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
Record Nr. UNINA-9910820309003321
Zadora Grzegorz  
Chichester, West Sussex : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui