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MARC Lista (tabellare)
Analyse statistique des donnees experimentales / / Konstantin Protassov
Analyse statistique des donnees experimentales / / Konstantin Protassov
Autore Protassov Konstantin
Edizione [1st ed.]
Pubbl/distr/stampa France, : EDP Sciences, [2002]
Descrizione fisica 1 online resource (150 p.)
Disciplina 519.5
Collana Collection Grenoble sciences
Soggetto topico Science - Statistical methods
Mathematical statistics
ISBN 9786610961894
9781417566624
1417566620
9781280961892
1280961899
9782759801138
2759801136
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Préface; Pourquoi les incertitudes existent-elles ?; Chapitre 1. Rappels sur la théorie des probabilités; Chapitre 2. Fonctions d'une variable aléatoire; Chapitre 3. Expériences avec un nombre limité de mesures; Chapitre 4. Ajustement des paramètres; Conclusion; Bibliographie; Index; Table des matières;
Record Nr. UNINA-9910969927503321
Protassov Konstantin  
France, : EDP Sciences, [2002]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Analyse statistique des données expérimentales [[electronic resource] /] / Konstantin Protassov
Analyse statistique des données expérimentales [[electronic resource] /] / Konstantin Protassov
Autore Protassov Konstantin
Pubbl/distr/stampa France, : EDP Sciences, [2002]
Descrizione fisica 1 online resource (150 p.)
Disciplina 519.5
Collana Collection Grenoble sciences
Soggetto topico Science - Statistical methods
Mathematical statistics
Soggetto genere / forma Electronic books.
ISBN 1-4175-6662-0
1-280-96189-9
9786610961894
2-7598-0113-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Préface; Pourquoi les incertitudes existent-elles ?; Chapitre 1. Rappels sur la théorie des probabilités; Chapitre 2. Fonctions d'une variable aléatoire; Chapitre 3. Expériences avec un nombre limité de mesures; Chapitre 4. Ajustement des paramètres; Conclusion; Bibliographie; Index; Table des matières;
Record Nr. UNINA-9910451223003321
Protassov Konstantin  
France, : EDP Sciences, [2002]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Analyse statistique des données expérimentales [[electronic resource] /] / Konstantin Protassov
Analyse statistique des données expérimentales [[electronic resource] /] / Konstantin Protassov
Autore Protassov Konstantin
Pubbl/distr/stampa France, : EDP Sciences, [2002]
Descrizione fisica 1 online resource (150 p.)
Disciplina 519.5
Collana Collection Grenoble sciences
Soggetto topico Science - Statistical methods
Mathematical statistics
ISBN 1-4175-6662-0
1-280-96189-9
9786610961894
2-7598-0113-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Préface; Pourquoi les incertitudes existent-elles ?; Chapitre 1. Rappels sur la théorie des probabilités; Chapitre 2. Fonctions d'une variable aléatoire; Chapitre 3. Expériences avec un nombre limité de mesures; Chapitre 4. Ajustement des paramètres; Conclusion; Bibliographie; Index; Table des matières;
Record Nr. UNINA-9910784295703321
Protassov Konstantin  
France, : EDP Sciences, [2002]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applying and interpreting statistics : a comprehensive guide / Glen McPherson
Applying and interpreting statistics : a comprehensive guide / Glen McPherson
Autore McPherson, Glen
Edizione [2nd ed.]
Pubbl/distr/stampa New York : Springer, c2001
Descrizione fisica xxviii, 640 p. : ill. ; 24 cm
Disciplina 507.2
Altri autori (Persone) McPherson, Glen.author
Collana Springer texts in statistics
Soggetto topico Research - Statistical methods
Science - Statistical methods
Statistics
Statistica - Manuali
ISBN 0387951105
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISALENTO-991001802659707536
McPherson, Glen  
New York : Springer, c2001
Materiale a stampa
Lo trovi qui: Univ. del Salento
Opac: Controlla la disponibilità qui
Basic statistical methods and models for the sciences / Judah Rosenblatt
Basic statistical methods and models for the sciences / Judah Rosenblatt
Autore Rosenblatt, Judah I. (Judah Isser), 1931-
Pubbl/distr/stampa Boca Raton, Fla. : Chapman & Hall/CRC, c2002
Descrizione fisica 282 p. : ill. ; 25 cm.
Disciplina 507.27
Soggetto topico Statistics
Science - Statistical methods
Science - statistics & numerical data
ISBN 158488147X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISALENTO-991003415419707536
Rosenblatt, Judah I. (Judah Isser), 1931-  
Boca Raton, Fla. : Chapman & Hall/CRC, c2002
Materiale a stampa
Lo trovi qui: Univ. del Salento
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Mathematics and statistics for science / / James Sneyd, Rachel M. Fewster, and Duncan McGillivray
Mathematics and statistics for science / / James Sneyd, Rachel M. Fewster, and Duncan McGillivray
Autore Sneyd James
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (952 pages)
Disciplina 507.2
Soggetto topico Science - Statistical methods
Science - Mathematics
Mathematics
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 3-031-05318-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996479366503316
Sneyd James  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Multiblock data fusion in statistics and machine learning : applications in the natural and life sciences / / Age K. Smilde, Tormod Næs, Kristian Hovde Liland
Multiblock data fusion in statistics and machine learning : applications in the natural and life sciences / / Age K. Smilde, Tormod Næs, Kristian Hovde Liland
Autore Smilde Age K.
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2022]
Descrizione fisica 1 online resource (418 pages)
Disciplina 519.52
Soggetto topico Science - Statistical methods
Soggetto genere / forma Electronic books.
ISBN 1-119-60097-9
1-119-60098-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Multiblock Data Fusion in Statistics and Machine Learning -- Contents -- Foreword -- Preface -- List of Figures -- List of Tables -- Part I Introductory Concepts and Theory -- chapnumcolor1 Introduction -- 1.1 Scope of the Book -- 1.2 Potential Audience -- 1.3 Types of Data and Analyses -- 1.3.1 Supervised and Unsupervised Analyses -- 1.3.2 High-, Mid- and Low-level Fusion -- 1.3.3 Dimension Reduction -- 1.3.4 Indirect Versus Direct Data -- 1.3.5 Heterogeneous Fusion -- 1.4 Examples -- 1.4.1 Metabolomics -- 1.4.2 Genomics -- 1.4.3 Systems Biology -- 1.4.4 Chemistry -- 1.4.5 Sensory Science -- 1.5 Goals of Analyses -- 1.6 Some History -- 1.7 Fundamental Choices -- 1.8 Common and Distinct Components -- 1.9 Overview and Links -- 1.10 Notation and Terminology -- 1.11 Abbreviations -- chapnumcolor2 Basic Theory and Concepts -- 2.i General Introduction -- 2.1 Component Models -- 2.1.1 General Idea of Component Models -- 2.1.2 Principal Component Analysis -- 2.1.3 Sparse PCA -- 2.1.4 Principal Component Regression -- 2.1.5 Partial Least Squares -- 2.1.6 Sparse PLS -- 2.1.7 Principal Covariates Regression -- 2.1.8 Redundancy Analysis -- 2.1.9 Comparing PLS, PCovR and RDA -- 2.1.10 Generalised Canonical Correlation Analysis -- 2.1.11 Simultaneous Component Analysis -- 2.2 Properties of Data -- 2.2.1 Data Theory -- 2.2.2 Scale-types -- 2.3 Estimation Methods -- 2.3.1 Least-squares Estimation -- 2.3.2 Maximum-likelihood Estimation -- 2.3.3 Eigenvalue Decomposition-based Methods -- 2.3.4 Covariance or Correlation-based Estimation Methods -- 2.3.5 Sequential Versus Simultaneous Methods -- 2.3.6 Homogeneous Versus Heterogeneous Fusion -- 2.4 Within- and Between-block Variation -- 2.4.1 Definition and Example -- 2.4.2 MAXBET Solution -- 2.4.3 MAXNEAR Solution -- 2.4.4 PLS2 Solution -- 2.4.5 CCA Solution -- 2.4.6 Comparing the Solutions.
2.4.7 PLS, RDA and CCA Revisited -- 2.5 Framework for Common and Distinct Components -- 2.6 Preprocessing -- 2.7 Validation -- 2.7.1 Outliers -- 2.7.1.1 Residuals -- 2.7.1.2 Leverage -- 2.7.2 Model Fit -- 2.7.3 Bias-variance Trade-off -- 2.7.4 Test Set Validation -- 2.7.5 Cross-validation -- 2.7.6 Permutation Testing -- 2.7.7 Jackknife and Bootstrap -- 2.7.8 Hyper-parameters and Penalties -- 2.8 Appendix -- chapnumcolor3 Structure of Multiblock Data -- 3.i General Introduction -- 3.1 Taxonomy -- 3.2 Skeleton of a Multiblock Data Set -- 3.2.1 Shared Sample Mode -- 3.2.2 Shared Variable Mode -- 3.2.3 Shared Variable or Sample Mode -- 3.2.4 Shared Variable and Sample Mode -- 3.3 Topology of a Multiblock Data Set -- 3.3.1 Unsupervised Analysis -- 3.3.2 Supervised Analysis -- 3.4 Linking Structures -- 3.4.1 Linking Structure for Unsupervised Analysis -- 3.4.2 Linking Structures for Supervised Analysis -- 3.5 Summary -- chapnumcolor4 Matrix Correlations -- 4.i General Introduction -- 4.1 Definition -- 4.2 Most Used Matrix Correlations -- 4.2.1 Inner Product Correlation -- 4.2.2 GCD coefficient -- 4.2.3 RV-coefficient -- 4.2.4 SMI-coefficient -- 4.3 Generic Framework of Matrix Correlations -- 4.4 Generalised Matrix Correlations -- 4.4.1 Generalised RV-coefficient -- 4.4.2 Generalised Association Coefficient -- 4.5 Partial Matrix Correlations -- 4.6 Conclusions and Recommendations -- 4.7 Open Issues -- Part II Selected Methods for Unsupervised and Supervised Topologies -- chapnumcolor5 Unsupervised Methods -- 5.i General Introduction -- 5.ii Relations to the General Framework -- 5.1 Shared Variable Mode -- 5.1.1 Only Common Variation -- 5.1.1.1 Simultaneous Component Analysis -- 5.1.1.2 Clustering and SCA -- 5.1.1.3 Multigroup Data Analysis -- 5.1.2 Common, Local, and Distinct Variation -- 5.1.2.1 Distinct and Common Components.
5.1.2.2 Multivariate Curve Resolution -- 5.2 Shared Sample Mode -- 5.2.1 Only Common Variation -- 5.2.1.1 SUM-PCA -- 5.2.1.2 Multiple Factor Analysis and STATIS -- 5.2.1.3 Generalised Canonical Analysis -- 5.2.1.4 Regularised Generalised Canonical Correlation Analysis -- 5.2.1.5 Exponential Family SCA -- 5.2.1.6 Optimal-scaling -- 5.2.2 Common, Local, and Distinct Variation -- 5.2.2.1 Joint and Individual Variation Explained -- 5.2.2.2 Distinct and Common Components -- 5.2.2.3 PCA-GCA -- 5.2.2.4 Advanced Coupled Matrix and Tensor Factorisation -- 5.2.2.5 Penalised-ESCA -- 5.2.2.6 Multivariate Curve Resolution -- 5.3 Generic Framework -- 5.3.1 Framework for Simultaneous Unsupervised Methods -- 5.3.1.1 Description of the Framework -- 5.3.1.2 Framework Applied to Simultaneous Unsupervised Data Analysis Methods -- 5.3.1.3 Framework of Common/Distinct Applied to Simultaneous Unsupervised Multiblock Data Analysis Methods -- 5.4 Conclusions and Recommendations -- 5.5 Open Issues -- chapnumcolor6 ASCA and Extensions -- 6.i General Introduction -- 6.ii Relations to the General Framework -- 6.1 ANOVA-Simultaneous Component Analysis -- 6.1.1 The ASCA Method -- 6.1.2 Validation of ASCA -- 6.1.2.1 Permutation Testing -- 6.1.2.2 Back-projection -- 6.1.2.3 Confidence Ellipsoids -- 6.1.3 The ASCA+ and LiMM-PCA Methods -- 6.2 Multilevel-SCA -- 6.3 Penalised-ASCA -- 6.4 Conclusions and Recommendations -- 6.5 Open Issues -- chapnumcolor7 Supervised Methods -- 7.i General Introduction -- 7.ii Relations to the General Framework -- 7.1 Multiblock Regression: General Perspectives -- 7.1.1 Model and Assumptions -- 7.1.2 Different Challenges and Aims -- 7.2 Multiblock PLS Regression -- 7.2.1 Standard Multiblock PLS Regression -- 7.2.2 MB-PLS Used for Classification -- 7.2.3 Sparse Multiblock PLS Regression (sMB-PLS).
7.3 The Family of SO-PLS Regression Methods (Sequential and Orthogonalised PLS Regression) -- 7.3.1 The SO-PLS Method -- 7.3.2 Order of Blocks -- 7.3.3 Interpretation Tools -- 7.3.4 Restricted PLS Components and their Application in SO-PLS -- 7.3.5 Validation and Component Selection -- 7.3.6 Relations to ANOVA -- 7.3.7 Extensions of SO-PLS to Handle Interactions Between Blocks -- 7.3.8 Further Applications of SO-PLS -- 7.3.9 Relations Between SO-PLS and ASCA -- 7.4 Parallel and Orthogonalised PLS (PO-PLS) Regression -- 7.5 Response Oriented Sequential Alternation -- 7.5.1 The ROSA Method -- 7.5.2 Validation -- 7.5.3 Interpretation -- 7.6 Conclusions and Recommendations -- 7.7 Open Issues -- Part III Methods for Complex Multiblock Structures -- chapnumcolor8 Complex Block Structures -- with Focus on L-Shape Relations -- 8.i General Introduction -- 8.ii Relations to the General Framework -- 8.1 Analysis of L-shape Data: General Perspectives -- 8.2 Sequential Procedures for L-shape Data Based on PLS/PCR and ANOVA -- 8.2.1 Interpretation of X1, Quantitative X2-data, Horizontal Axis First -- 8.2.2 Interpretation of X1, Categorical X2-data, Horizontal Axis First -- 8.2.3 Analysis of Segments/Clusters of X1 Data -- 8.3 The L-PLS Method for Joint Estimation of Blocks in L-shape Data -- 8.3.1 The Original L-PLS Method, Endo-L-PLS -- 8.3.2 Exo- Versus Endo-L-PLS -- 8.4 Modifications of the Original L-PLS Idea -- 8.4.1 Weighting Information from X3 and X1 in L-PLS Using a Parameter "α -- 8.4.2 Three-blocks Bifocal PLS -- 8.5 Alternative L-shape Data Analysis Methods -- 8.5.1 Principal Component Analysis with External Information -- 8.5.2 A Simple PCA Based Procedure for Using Unlabelled Data in Calibration -- 8.5.3 Multivariate Curve Resolution for Incomplete Data -- 8.5.4 An Alternative Approach in Consumer Science Based on Correlations Between X3 and X1.
8.6 Domino PLS and More Complex Data Structures -- 8.7 Conclusions and Recommendations -- 8.8 Open Issues -- Part IV Alternative Methods for Unsupervised and Supervised Topologies -- chapnumcolor9 Alternative Unsupervised Methods -- 9.i General Introduction -- 9.ii Relationship to the General Framework -- 9.1 Shared Variable Mode -- 9.2 Shared Sample Mode -- 9.2.1 Only Common Variation -- 9.2.1.1 DIABLO -- 9.2.1.2 Generalised Coupled Tensor Factorisation -- 9.2.1.3 Representation Matrices -- 9.2.1.4 Extended PCA -- 9.2.2 Common, Local, and Distinct Variation -- 9.2.2.1 Generalised SVD -- 9.2.2.2 Structural Learning and Integrative Decomposition -- 9.2.2.3 Bayesian Inter-battery Factor Analysis -- 9.2.2.4 Group Factor Analysis -- 9.2.2.5 OnPLS -- 9.2.2.6 Generalised Association Study -- 9.2.2.7 Multi-Omics Factor Analysis -- 9.3 Two Shared Modes and Only Common Variation -- 9.3.1 Generalised Procrustes Analysis -- 9.3.2 Three-way Methods -- 9.4 Conclusions and Recommendations -- 9.4.1 Open Issues -- chapnumcolor10 Alternative Supervised Methods -- 10.i General Introduction -- 10.ii Relations to the General Framework -- 10.1 Model and Focus -- 10.2 Extension of PCovR -- 10.2.1 Sparse Multiblock Principal Covariates Regression, Sparse PCovR -- 10.2.2 Multiway Multiblock Covariates Regression -- 10.3 Multiblock Redundancy Analysis -- 10.3.1 Standard Multiblock Redundancy Analysis -- 10.3.2 Sparse Multiblock Redundancy Analysis -- 10.4 Miscellaneous Multiblock Regression Methods -- 10.4.1 Multiblock Variance Partitioning -- 10.4.2 Network Induced Supervised Learning -- 10.4.3 Common Dimensions for Multiblock Regression -- 10.5 Modifications and Extensions of the SO-PLS Method -- 10.5.1 Extensions of SO-PLS to Three-Way Data -- 10.5.2 Variable Selection for SO-PLS -- 10.5.3 More Complicated Error Structure for SO-PLS.
10.5.4 SO-PLS Used for Path Modelling.
Record Nr. UNINA-9910566700603321
Smilde Age K.  
Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Multiblock data fusion in statistics and machine learning : applications in the natural and life sciences / / Age K. Smilde, Tormod Næs, Kristian Hovde Liland
Multiblock data fusion in statistics and machine learning : applications in the natural and life sciences / / Age K. Smilde, Tormod Næs, Kristian Hovde Liland
Autore Smilde Age K.
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2022]
Descrizione fisica 1 online resource (418 pages)
Disciplina 519.52
Soggetto topico Science - Statistical methods
ISBN 1-119-60097-9
1-119-60098-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Multiblock Data Fusion in Statistics and Machine Learning -- Contents -- Foreword -- Preface -- List of Figures -- List of Tables -- Part I Introductory Concepts and Theory -- chapnumcolor1 Introduction -- 1.1 Scope of the Book -- 1.2 Potential Audience -- 1.3 Types of Data and Analyses -- 1.3.1 Supervised and Unsupervised Analyses -- 1.3.2 High-, Mid- and Low-level Fusion -- 1.3.3 Dimension Reduction -- 1.3.4 Indirect Versus Direct Data -- 1.3.5 Heterogeneous Fusion -- 1.4 Examples -- 1.4.1 Metabolomics -- 1.4.2 Genomics -- 1.4.3 Systems Biology -- 1.4.4 Chemistry -- 1.4.5 Sensory Science -- 1.5 Goals of Analyses -- 1.6 Some History -- 1.7 Fundamental Choices -- 1.8 Common and Distinct Components -- 1.9 Overview and Links -- 1.10 Notation and Terminology -- 1.11 Abbreviations -- chapnumcolor2 Basic Theory and Concepts -- 2.i General Introduction -- 2.1 Component Models -- 2.1.1 General Idea of Component Models -- 2.1.2 Principal Component Analysis -- 2.1.3 Sparse PCA -- 2.1.4 Principal Component Regression -- 2.1.5 Partial Least Squares -- 2.1.6 Sparse PLS -- 2.1.7 Principal Covariates Regression -- 2.1.8 Redundancy Analysis -- 2.1.9 Comparing PLS, PCovR and RDA -- 2.1.10 Generalised Canonical Correlation Analysis -- 2.1.11 Simultaneous Component Analysis -- 2.2 Properties of Data -- 2.2.1 Data Theory -- 2.2.2 Scale-types -- 2.3 Estimation Methods -- 2.3.1 Least-squares Estimation -- 2.3.2 Maximum-likelihood Estimation -- 2.3.3 Eigenvalue Decomposition-based Methods -- 2.3.4 Covariance or Correlation-based Estimation Methods -- 2.3.5 Sequential Versus Simultaneous Methods -- 2.3.6 Homogeneous Versus Heterogeneous Fusion -- 2.4 Within- and Between-block Variation -- 2.4.1 Definition and Example -- 2.4.2 MAXBET Solution -- 2.4.3 MAXNEAR Solution -- 2.4.4 PLS2 Solution -- 2.4.5 CCA Solution -- 2.4.6 Comparing the Solutions.
2.4.7 PLS, RDA and CCA Revisited -- 2.5 Framework for Common and Distinct Components -- 2.6 Preprocessing -- 2.7 Validation -- 2.7.1 Outliers -- 2.7.1.1 Residuals -- 2.7.1.2 Leverage -- 2.7.2 Model Fit -- 2.7.3 Bias-variance Trade-off -- 2.7.4 Test Set Validation -- 2.7.5 Cross-validation -- 2.7.6 Permutation Testing -- 2.7.7 Jackknife and Bootstrap -- 2.7.8 Hyper-parameters and Penalties -- 2.8 Appendix -- chapnumcolor3 Structure of Multiblock Data -- 3.i General Introduction -- 3.1 Taxonomy -- 3.2 Skeleton of a Multiblock Data Set -- 3.2.1 Shared Sample Mode -- 3.2.2 Shared Variable Mode -- 3.2.3 Shared Variable or Sample Mode -- 3.2.4 Shared Variable and Sample Mode -- 3.3 Topology of a Multiblock Data Set -- 3.3.1 Unsupervised Analysis -- 3.3.2 Supervised Analysis -- 3.4 Linking Structures -- 3.4.1 Linking Structure for Unsupervised Analysis -- 3.4.2 Linking Structures for Supervised Analysis -- 3.5 Summary -- chapnumcolor4 Matrix Correlations -- 4.i General Introduction -- 4.1 Definition -- 4.2 Most Used Matrix Correlations -- 4.2.1 Inner Product Correlation -- 4.2.2 GCD coefficient -- 4.2.3 RV-coefficient -- 4.2.4 SMI-coefficient -- 4.3 Generic Framework of Matrix Correlations -- 4.4 Generalised Matrix Correlations -- 4.4.1 Generalised RV-coefficient -- 4.4.2 Generalised Association Coefficient -- 4.5 Partial Matrix Correlations -- 4.6 Conclusions and Recommendations -- 4.7 Open Issues -- Part II Selected Methods for Unsupervised and Supervised Topologies -- chapnumcolor5 Unsupervised Methods -- 5.i General Introduction -- 5.ii Relations to the General Framework -- 5.1 Shared Variable Mode -- 5.1.1 Only Common Variation -- 5.1.1.1 Simultaneous Component Analysis -- 5.1.1.2 Clustering and SCA -- 5.1.1.3 Multigroup Data Analysis -- 5.1.2 Common, Local, and Distinct Variation -- 5.1.2.1 Distinct and Common Components.
5.1.2.2 Multivariate Curve Resolution -- 5.2 Shared Sample Mode -- 5.2.1 Only Common Variation -- 5.2.1.1 SUM-PCA -- 5.2.1.2 Multiple Factor Analysis and STATIS -- 5.2.1.3 Generalised Canonical Analysis -- 5.2.1.4 Regularised Generalised Canonical Correlation Analysis -- 5.2.1.5 Exponential Family SCA -- 5.2.1.6 Optimal-scaling -- 5.2.2 Common, Local, and Distinct Variation -- 5.2.2.1 Joint and Individual Variation Explained -- 5.2.2.2 Distinct and Common Components -- 5.2.2.3 PCA-GCA -- 5.2.2.4 Advanced Coupled Matrix and Tensor Factorisation -- 5.2.2.5 Penalised-ESCA -- 5.2.2.6 Multivariate Curve Resolution -- 5.3 Generic Framework -- 5.3.1 Framework for Simultaneous Unsupervised Methods -- 5.3.1.1 Description of the Framework -- 5.3.1.2 Framework Applied to Simultaneous Unsupervised Data Analysis Methods -- 5.3.1.3 Framework of Common/Distinct Applied to Simultaneous Unsupervised Multiblock Data Analysis Methods -- 5.4 Conclusions and Recommendations -- 5.5 Open Issues -- chapnumcolor6 ASCA and Extensions -- 6.i General Introduction -- 6.ii Relations to the General Framework -- 6.1 ANOVA-Simultaneous Component Analysis -- 6.1.1 The ASCA Method -- 6.1.2 Validation of ASCA -- 6.1.2.1 Permutation Testing -- 6.1.2.2 Back-projection -- 6.1.2.3 Confidence Ellipsoids -- 6.1.3 The ASCA+ and LiMM-PCA Methods -- 6.2 Multilevel-SCA -- 6.3 Penalised-ASCA -- 6.4 Conclusions and Recommendations -- 6.5 Open Issues -- chapnumcolor7 Supervised Methods -- 7.i General Introduction -- 7.ii Relations to the General Framework -- 7.1 Multiblock Regression: General Perspectives -- 7.1.1 Model and Assumptions -- 7.1.2 Different Challenges and Aims -- 7.2 Multiblock PLS Regression -- 7.2.1 Standard Multiblock PLS Regression -- 7.2.2 MB-PLS Used for Classification -- 7.2.3 Sparse Multiblock PLS Regression (sMB-PLS).
7.3 The Family of SO-PLS Regression Methods (Sequential and Orthogonalised PLS Regression) -- 7.3.1 The SO-PLS Method -- 7.3.2 Order of Blocks -- 7.3.3 Interpretation Tools -- 7.3.4 Restricted PLS Components and their Application in SO-PLS -- 7.3.5 Validation and Component Selection -- 7.3.6 Relations to ANOVA -- 7.3.7 Extensions of SO-PLS to Handle Interactions Between Blocks -- 7.3.8 Further Applications of SO-PLS -- 7.3.9 Relations Between SO-PLS and ASCA -- 7.4 Parallel and Orthogonalised PLS (PO-PLS) Regression -- 7.5 Response Oriented Sequential Alternation -- 7.5.1 The ROSA Method -- 7.5.2 Validation -- 7.5.3 Interpretation -- 7.6 Conclusions and Recommendations -- 7.7 Open Issues -- Part III Methods for Complex Multiblock Structures -- chapnumcolor8 Complex Block Structures -- with Focus on L-Shape Relations -- 8.i General Introduction -- 8.ii Relations to the General Framework -- 8.1 Analysis of L-shape Data: General Perspectives -- 8.2 Sequential Procedures for L-shape Data Based on PLS/PCR and ANOVA -- 8.2.1 Interpretation of X1, Quantitative X2-data, Horizontal Axis First -- 8.2.2 Interpretation of X1, Categorical X2-data, Horizontal Axis First -- 8.2.3 Analysis of Segments/Clusters of X1 Data -- 8.3 The L-PLS Method for Joint Estimation of Blocks in L-shape Data -- 8.3.1 The Original L-PLS Method, Endo-L-PLS -- 8.3.2 Exo- Versus Endo-L-PLS -- 8.4 Modifications of the Original L-PLS Idea -- 8.4.1 Weighting Information from X3 and X1 in L-PLS Using a Parameter "α -- 8.4.2 Three-blocks Bifocal PLS -- 8.5 Alternative L-shape Data Analysis Methods -- 8.5.1 Principal Component Analysis with External Information -- 8.5.2 A Simple PCA Based Procedure for Using Unlabelled Data in Calibration -- 8.5.3 Multivariate Curve Resolution for Incomplete Data -- 8.5.4 An Alternative Approach in Consumer Science Based on Correlations Between X3 and X1.
8.6 Domino PLS and More Complex Data Structures -- 8.7 Conclusions and Recommendations -- 8.8 Open Issues -- Part IV Alternative Methods for Unsupervised and Supervised Topologies -- chapnumcolor9 Alternative Unsupervised Methods -- 9.i General Introduction -- 9.ii Relationship to the General Framework -- 9.1 Shared Variable Mode -- 9.2 Shared Sample Mode -- 9.2.1 Only Common Variation -- 9.2.1.1 DIABLO -- 9.2.1.2 Generalised Coupled Tensor Factorisation -- 9.2.1.3 Representation Matrices -- 9.2.1.4 Extended PCA -- 9.2.2 Common, Local, and Distinct Variation -- 9.2.2.1 Generalised SVD -- 9.2.2.2 Structural Learning and Integrative Decomposition -- 9.2.2.3 Bayesian Inter-battery Factor Analysis -- 9.2.2.4 Group Factor Analysis -- 9.2.2.5 OnPLS -- 9.2.2.6 Generalised Association Study -- 9.2.2.7 Multi-Omics Factor Analysis -- 9.3 Two Shared Modes and Only Common Variation -- 9.3.1 Generalised Procrustes Analysis -- 9.3.2 Three-way Methods -- 9.4 Conclusions and Recommendations -- 9.4.1 Open Issues -- chapnumcolor10 Alternative Supervised Methods -- 10.i General Introduction -- 10.ii Relations to the General Framework -- 10.1 Model and Focus -- 10.2 Extension of PCovR -- 10.2.1 Sparse Multiblock Principal Covariates Regression, Sparse PCovR -- 10.2.2 Multiway Multiblock Covariates Regression -- 10.3 Multiblock Redundancy Analysis -- 10.3.1 Standard Multiblock Redundancy Analysis -- 10.3.2 Sparse Multiblock Redundancy Analysis -- 10.4 Miscellaneous Multiblock Regression Methods -- 10.4.1 Multiblock Variance Partitioning -- 10.4.2 Network Induced Supervised Learning -- 10.4.3 Common Dimensions for Multiblock Regression -- 10.5 Modifications and Extensions of the SO-PLS Method -- 10.5.1 Extensions of SO-PLS to Three-Way Data -- 10.5.2 Variable Selection for SO-PLS -- 10.5.3 More Complicated Error Structure for SO-PLS.
10.5.4 SO-PLS Used for Path Modelling.
Record Nr. UNINA-9910677590703321
Smilde Age K.  
Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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The nature of scientific evidence [[electronic resource] ] : statistical, philosophical and empirical considerations / / edited by Mark L. Taper and Subhash R. Lele
The nature of scientific evidence [[electronic resource] ] : statistical, philosophical and empirical considerations / / edited by Mark L. Taper and Subhash R. Lele
Pubbl/distr/stampa Chicago, : University of Chicago Press, 2004
Descrizione fisica 1 online resource (586 p.)
Disciplina 507/.2
Altri autori (Persone) TaperMark L. <1952->
LeleSubhash
Soggetto topico Science - Statistical methods
Science - Methodology
Soggetto genere / forma Electronic books.
ISBN 1-283-05860-X
9786613058607
0-226-78958-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto pt. 1. Scientific process -- pt. 2. Logics of evidence -- pt. 3. Realities of nature -- pt. 4. Science, opinion and evidence -- pt. 5. Models, realities and evidence -- pt. 6. Conclusion.
Record Nr. UNINA-9910460294903321
Chicago, : University of Chicago Press, 2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
The nature of scientific evidence [[electronic resource] ] : statistical, philosophical and empirical considerations / / edited by Mark L. Taper and Subhash R. Lele
The nature of scientific evidence [[electronic resource] ] : statistical, philosophical and empirical considerations / / edited by Mark L. Taper and Subhash R. Lele
Pubbl/distr/stampa Chicago, : University of Chicago Press, 2004
Descrizione fisica 1 online resource (586 p.)
Disciplina 507/.2
Altri autori (Persone) TaperMark L. <1952->
LeleSubhash
Soggetto topico Science - Statistical methods
Science - Methodology
Soggetto non controllato scientific evidence, data, statistics, quantifiable, hypotheses, models, inference, methodology, inquiry, research, ecology, experiment, observations, likelihood, replication, ecosystem, science, nonfiction, opinion, subjective probability, bayesian analysis, model adequacy, expertise, scholarship, academia, technology, simulation
ISBN 1-283-05860-X
9786613058607
0-226-78958-6
Classificazione WC 7600
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto pt. 1. Scientific process -- pt. 2. Logics of evidence -- pt. 3. Realities of nature -- pt. 4. Science, opinion and evidence -- pt. 5. Models, realities and evidence -- pt. 6. Conclusion.
Record Nr. UNINA-9910785403303321
Chicago, : University of Chicago Press, 2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui