{"product_id":"multi-way-analysis-applications-in-the-chemical-sciences-hardback-9780471986911","title":"Multi-way Analysis; Applications in the Chemical Sciences (Hardback) 9780471986911","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMulti-way Analysis\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eApplications in the Chemical Sciences\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eAge K. Smilde (Author), Rasmus Bro (Author), Paul Geladi (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780471986911, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 20 August 2004\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e400 pages\u003cbr\u003e24.4 x 16.8 x 2.6 cm, 0.879 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\"...covers some very important topics in modern chemical data analysis...this book is a reasonable place to start. I know of no others.\" (\u003ci\u003eTechnometrics\u003c\/i\u003e, November 2005)  \u003cp\u003e\"It will be a valuable addition to the library collections at universities and companies.\" (\u003ci\u003eE-STREAMS\u003c\/i\u003e, September 2005)\u003c\/p\u003e \u003cp\u003e\"...extremely useful...\" (Journal of Chemometrics, Vol 19 (2), February 2005)\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eDie Mehrwegeanalyse ist eine moderne Methode der Datenanalyse, die sich in chemischen und industriellen Labors zunehmender Beliebtheit erfreut. Während etliche Werke auf dem Markt sind, die sich mit den mathematischen Grundlagen des Verfahrens beschäftigen, fehlte bisher eine speziell für den Chemiker geeignete praxisbezogene Einführung. \"Multi-Way Analysis in Chemistry\" schließt diese Lücke und bietet eine ideale Mischung aus theoretischen Hintergrundinformationen und chemischen Anwendungen.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eForeword ix\u003c\/p\u003e \u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003eNomenclature and Conventions xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 What is multi-way analysis? 1\u003c\/p\u003e \u003cp\u003e1.2 Conceptual aspects of multi-way data analysis 1\u003c\/p\u003e \u003cp\u003e1.3 Hierarchy of multivariate data structures in chemistry 5\u003c\/p\u003e \u003cp\u003e1.4 Principal component analysis and PARAFAC 11\u003c\/p\u003e \u003cp\u003e1.5 Summary 12\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Array definitions and properties 13\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 13\u003c\/p\u003e \u003cp\u003e2.2 Rows, columns and tubes; frontal, lateral and horizontal slices 13\u003c\/p\u003e \u003cp\u003e2.3 Elementary operations 15\u003c\/p\u003e \u003cp\u003e2.4 Linearity concepts 21\u003c\/p\u003e \u003cp\u003e2.5 Rank of two-way arrays 22\u003c\/p\u003e \u003cp\u003e2.6 Rank of three-way arrays 28\u003c\/p\u003e \u003cp\u003e2.7 Algebra of multi-way analysis 32\u003c\/p\u003e \u003cp\u003e2.8 Summary 34\u003c\/p\u003e \u003cp\u003eAppendix 2.A 34\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Two-way component and regression models 35\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Models for two-way one-block data analysis: component models 35\u003c\/p\u003e \u003cp\u003e3.2 Models for two-way two-block data analysis: regression models 46\u003c\/p\u003e \u003cp\u003e3.3 Summary 53\u003c\/p\u003e \u003cp\u003eAppendix 3.A: some PCA results 54\u003c\/p\u003e \u003cp\u003eAppendix 3.B: PLS algorithms 55\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Three-way component and regression models 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Historical introduction to multi-way models 57\u003c\/p\u003e \u003cp\u003e4.2 Models for three-way one-block data: three-way component models 59\u003c\/p\u003e \u003cp\u003e4.3 Models for three-way two-block data: three-way regression models 76\u003c\/p\u003e \u003cp\u003e4.4 Summary 83\u003c\/p\u003e \u003cp\u003eAppendix 4.A: alternative notation for the PARAFAC model 84\u003c\/p\u003e \u003cp\u003eAppendix 4.B: alternative notations for the Tucker3 model 86\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Some properties of three-way component models 89\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Relationships between three-way component models 89\u003c\/p\u003e \u003cp\u003e5.2 Rotational freedom and uniqueness in three-way component models 98\u003c\/p\u003e \u003cp\u003e5.3 Properties of Tucker3 models 106\u003c\/p\u003e \u003cp\u003e5.4 Degeneracy problem in PARAFAC models 107\u003c\/p\u003e \u003cp\u003e5.5 Summary 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Algorithms 111\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 111\u003c\/p\u003e \u003cp\u003e6.2 Optimization techniques 111\u003c\/p\u003e \u003cp\u003e6.3 PARAFAC algorithms 113\u003c\/p\u003e \u003cp\u003e6.4 Tucker3 algorithms 119\u003c\/p\u003e \u003cp\u003e6.5 Tucker2 and Tucker1 algorithms 123\u003c\/p\u003e \u003cp\u003e6.6 Multi-linear partial least squares regression 124\u003c\/p\u003e \u003cp\u003e6.7 Multi-way covariates regression models 128\u003c\/p\u003e \u003cp\u003e6.8 Core rotation in Tucker3 models 130\u003c\/p\u003e \u003cp\u003e6.9 Handling missing data 131\u003c\/p\u003e \u003cp\u003e6.10 Imposing non-negativity 135\u003c\/p\u003e \u003cp\u003e6.11 Summary 136\u003c\/p\u003e \u003cp\u003eAppendix 6.A: closed-form solution for the PARAFAC model 136\u003c\/p\u003e \u003cp\u003eAppendix 6.B: proof that the weights in trilinear PLS1 can be obtained from a singular value decomposition 144\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Validation and diagnostics 145\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 What is validation? 145\u003c\/p\u003e \u003cp\u003e7.2 Test-set and cross-validation 147\u003c\/p\u003e \u003cp\u003e7.3 Selecting which model to use 154\u003c\/p\u003e \u003cp\u003e7.4 Selecting the number of components 156\u003c\/p\u003e \u003cp\u003e7.5 Residual and influence analysis 166\u003c\/p\u003e \u003cp\u003e7.6 Summary 173\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Visualization 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 175\u003c\/p\u003e \u003cp\u003e8.2 History of plotting in three-way analysis 179\u003c\/p\u003e \u003cp\u003e8.3 History of plotting in chemical three-way analysis 180\u003c\/p\u003e \u003cp\u003e8.4 Scree plots 180\u003c\/p\u003e \u003cp\u003e8.5 Line plots 184\u003c\/p\u003e \u003cp\u003e8.6 Scatter plots 190\u003c\/p\u003e \u003cp\u003e8.7 Problems with scatter plots 192\u003c\/p\u003e \u003cp\u003e8.8 Image analysis 201\u003c\/p\u003e \u003cp\u003e8.9 Dendrograms 202\u003c\/p\u003e \u003cp\u003e8.10 Visualizing the Tucker core array 204\u003c\/p\u003e \u003cp\u003e8.11 Joint plots 205\u003c\/p\u003e \u003cp\u003e8.12 Residual plots 216\u003c\/p\u003e \u003cp\u003e8.13 Leverage plots 216\u003c\/p\u003e \u003cp\u003e8.14 Visualization of large data sets 216\u003c\/p\u003e \u003cp\u003e8.15 Summary 219\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Preprocessing 221\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Background 221\u003c\/p\u003e \u003cp\u003e9.2 Two-way centering 228\u003c\/p\u003e \u003cp\u003e9.3 Two-way scaling 232\u003c\/p\u003e \u003cp\u003e9.4 Simultaneous two-way centering and scaling 238\u003c\/p\u003e \u003cp\u003e9.5 Three-way preprocessing 239\u003c\/p\u003e \u003cp\u003e9.6 Summary 244\u003c\/p\u003e \u003cp\u003eAppendix 9.A: other types of preprocessing 245\u003c\/p\u003e \u003cp\u003eAppendix 9.B: geometric view of centering 247\u003c\/p\u003e \u003cp\u003eAppendix 9.C: fitting bilinear model plus offsets across one mode equals fitting a bilinear model to centered data 249\u003c\/p\u003e \u003cp\u003eAppendix 9.D: rank reduction and centering 250\u003c\/p\u003e \u003cp\u003eAppendix 9.E: centering data with missing values 251\u003c\/p\u003e \u003cp\u003eAppendix 9.F: incorrect centering introduces artificial variation 251\u003c\/p\u003e \u003cp\u003eAppendix 9.G: alternatives to centering 254\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Applications 257\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 257\u003c\/p\u003e \u003cp\u003e10.2 Curve resolution of fluorescence data 259\u003c\/p\u003e \u003cp\u003e10.3 Second-order calibration 276\u003c\/p\u003e \u003cp\u003e10.4 Multi-way regression 285\u003c\/p\u003e \u003cp\u003e10.5 Process chemometrics 288\u003c\/p\u003e \u003cp\u003e10.6 Exploratory analysis in chromatography 302\u003c\/p\u003e \u003cp\u003e10.7 Exploratory analysis in environmental sciences 312\u003c\/p\u003e \u003cp\u003e10.8 Exploratory analysis of designed data 323\u003c\/p\u003e \u003cp\u003e10.9 Analysis of variance of data with complex interactions 340\u003c\/p\u003e \u003cp\u003eAppendix 10.A: an illustration of the generalized rank annihilation method 346\u003c\/p\u003e \u003cp\u003eAppendix 10.B: other types of second-order calibration problems 347\u003c\/p\u003e \u003cp\u003eAppendix 10.C: the multiple standards calibration model of the second-order calibration example 349\u003c\/p\u003e \u003cp\u003eReferences 351\u003c\/p\u003e \u003cp\u003eIndex 371\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Chemistry [\u003ca title=\"See our other books on Chemistry\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Chemistry%20%5BPN%5D%22\"\u003ePN\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52316745203992,"sku":"9780471986911","price":116.59,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780471986911.jpg?v=1781823662","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/multi-way-analysis-applications-in-the-chemical-sciences-hardback-9780471986911","provider":"Freshly Printed Books","version":"1.0","type":"link"}