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Data Analysis and Classification

E-BookPDF1 - PDF WatermarkE-Book
482 Seiten
Englisch
Springer Berlin Heidelbergerschienen am14.03.20102010
The volume provides results from the latest methodological developments in data analysis and classification and highlights new emerging subjects within the field. It contains articles about statistical models, classification, cluster analysis, multidimensional scaling, multivariate analysis, latent variables, knowledge extraction from temporal data, financial and economic applications, and missing values. Papers cover both theoretical and empirical aspects.mehr
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Produkt

KlappentextThe volume provides results from the latest methodological developments in data analysis and classification and highlights new emerging subjects within the field. It contains articles about statistical models, classification, cluster analysis, multidimensional scaling, multivariate analysis, latent variables, knowledge extraction from temporal data, financial and economic applications, and missing values. Papers cover both theoretical and empirical aspects.
Details
Weitere ISBN/GTIN9783642037399
ProduktartE-Book
EinbandartE-Book
FormatPDF
Format Hinweis1 - PDF Watermark
FormatE107
Erscheinungsjahr2010
Erscheinungsdatum14.03.2010
Auflage2010
ReiheStudies in Classification, Data Analysis, and Knowledge Organization
Seiten482 Seiten
SpracheEnglisch
IllustrationenXXII, 482 p. 109 illus.
Artikel-Nr.1442903
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
1;Preface;5
2;List of Referees;8
3;Contents;9
4;Contributors;15
5;Part I Key-note;21
5.1;Clustering of High-Dimensional and Correlated Data;22
5.1.1;1 Introduction;22
5.1.2;2 Definition of Mixture Models;23
5.1.3;3 Maximum Likelihood Estimation;24
5.1.4;4 Choice of Starting Values for the EM Algorithm;24
5.1.5;5 Clustering via Normal Mixtures;25
5.1.6;6 Factor Analysis Model for Dimension Reduction;26
5.1.7;7 Some Recent Extensions for High-Dimensional Data;27
5.1.8;8 Mixtures of Normal Components with Random Effects;28
5.1.9;References;30
5.2;Statistical Methods for Cryptography;31
5.2.1;1 Introduction;31
5.2.1.1;1.1 Different disciplines in cryptography;32
5.2.2;2 Prime Numbers;33
5.2.2.1;2.1 Tests of primality;33
5.2.2.2;2.2 Deterministic tests;34
5.2.2.3;2.3 Some deterministic tests;35
5.2.3;3 The Sum Modulo m of Statistical Variables;36
5.2.4;References;39
6;Part II Cluster Analysis;40
6.1;An Algorithm for Earthquakes Clustering Based on MaximumLikelihood;41
6.1.1;1 Introduction;41
6.1.2;2 Conditional Intensity Function of the Clustering Procedure;42
6.1.2.1;2.1 The ETAS Model;43
6.1.2.2;2.2 Intensity Function for a Particular ClusteredInhomogeneous Poisson Process;44
6.1.3;3 The Proposed Clustering Method;45
6.1.3.1;3.1 Finding a Candidate Cluster and Likelihood Changes;45
6.1.3.2;3.2 The Algorithm of Clustering;46
6.1.4;4 Application to a Real Catalog and Final Remarks;47
6.1.5;References;48
6.2;A Two-Step Iterative Procedure for Clustering of Binary Sequences;49
6.2.1;1 Introduction;49
6.2.2;2 Clustering and Dimensionality Reduction;50
6.2.3;3 Example;53
6.2.4;References;56
6.3;Clustering Linear Models Using Wasserstein Distance;57
6.3.1;1 Introduction;57
6.3.2;2 Input Data and the Clustering Algorithm;58
6.3.3;3 Dynamic Clustering of Linear Models;59
6.3.3.1;3.1 Wasserstein Distance for Distributions ;60
6.3.3.2;3.2 Representation and Allocation Functions;61
6.3.4;4 An Application Using Bank of Italy Household Survey Data;62
6.3.5;5 Conclusions and Perspectives;64
6.3.6;References;64
6.4;Comparing Approaches for Clustering Mixed Mode Data: An Application in Marketing Research;65
6.4.1;1 Introduction;65
6.4.2;2 Obtaining Partitions with Mixed Mode Data;66
6.4.3;3 Some Illustrative Applications;67
6.4.4;4 Discussion;72
6.4.5;References;72
6.5;The Progressive Single Linkage Algorithm Based on Minkowski Ultrametrics;74
6.5.1;1 Introduction;74
6.5.2;2 Ultrametric Approximations;75
6.5.3;3 The Progressive Single Linkage Algorithm;76
6.5.4;4 Some Applications to Real Data;77
6.5.5;5 Conclusions;79
6.5.6;Appendix;80
6.5.7;References;81
6.6;Visualization of Model-Based Clustering Structures;82
6.6.1;1 Introduction;82
6.6.2;2 Dimension Reduction for Model-Based Clustering;83
6.6.3;3 Visualization of Clustering Structures;84
6.6.4;4 Examples;86
6.6.4.1;4.1 Overlapping Clusters with Unconstrained Covariances;86
6.6.4.2;4.2 High Dimensional Mixture of Two Normal Distributions;87
6.6.4.3;4.3 Wisconsin Diagnostic Breast Cancer Data;87
6.6.5;5 Comments and Extensions;89
6.6.6;References;89
7;Part III Multidimensional Scaling;91
7.1;Models for Asymmetry in Proximity Data;92
7.1.1;1 Introduction;92
7.1.2;2 A Class of Scalar Product Models;92
7.1.3;3 The Analysis of Skew-Symmetry with External Information;94
7.1.4;4 Conclusions;96
7.1.5;References;97
7.2;Intimate Femicide in Italy: A Model to Classify How KillingsHappened;98
7.2.1;1 Introduction;98
7.2.2;2 National and International Scenario;99
7.2.3;3 Data and Method;101
7.2.4;4 The Main Results;102
7.2.5;References;104
7.3;Two-Dimensional Centrality of Asymmetric Social Network;105
7.3.1;1 Introduction;105
7.3.2;2 The Procedure;106
7.3.3;3 The Data;107
7.3.4;4 The Analysis;107
7.3.5;5 Result;108
7.3.6;6 Discussion;109
7.3.7;References;111
7.4;The Forward Search for Classical Multidimensional Scaling When the Starting Data Matrix Is Known;113
7.4.1;1 Introduction;113
7.4.2;2 Classical Multidimensional Scaling and the Forward Search;114
7.4.3;3 A Case Study: Linosa Dataset;116
7.4.4;4 Conclusions;120
7.4.5;References;121
8;Part IV Multivariate Analysis and Application;122
8.1;Discriminant Analysis on Mixed Predictors;123
8.1.1;1 Introduction;123
8.1.2;2 Mixed Discriminant Predictors;124
8.1.3;3 Application Example;126
8.1.3.1;3.1 Predictor Analysis;126
8.1.3.2;3.2 Discriminant Analysis;128
8.1.3.3;3.3 Comparison;128
8.1.4;4 Conclusion;130
8.1.5;References;130
8.2;A Statistical Calibration Model for Affymetrix Probe Level Data;131
8.2.1;1 Introduction;131
8.2.2;2 A Brief Review on preprocessing Methods;132
8.2.3;3 The Proposed Calibration Method;133
8.2.4;4 A Comparison with the Most Popular Methods;135
8.2.5;5 Conclusions;137
8.2.6;References;138
8.3;A Proposal to Fuzzify Categorical Variables in Operational Risk Management;139
8.3.1;1 Fuzzy Approach;139
8.3.2;2 The Problem;140
8.3.3;3 The Proposal;141
8.3.4;4 Results;143
8.3.5;5 Conclusions;144
8.3.6;References;145
8.4;Common Optimal Scaling for Customer Satisfaction Models: A Point to Cobb-Douglas' Form;146
8.4.1;1 Features of a Customer Satisfaction model;146
8.4.2;2 Categorical Regression with Common Optimal Scaling;147
8.4.2.1;2.1 The Pattern of the Model;148
8.4.2.1.1;2.1.1 The Algorithm of the Parameters Estimation;149
8.4.3;3 Multiplicative Models for CS;149
8.4.3.1;3.1 Some Observations;150
8.4.4;4 A Theory About Overall CS;151
8.4.5;5 Conclusions;152
8.4.6;References;152
8.5;Structural Neural Networks for Modeling Customer Satisfaction;154
8.5.1;1 Introduction;154
8.5.2;2 Customer Satisfaction and PLS Path Modeling;155
8.5.3;3 Customer Satisfaction and Neural Networks;156
8.5.4;4 A Structural Neural Network for Modeling CS;157
8.5.5;5 Concluding Remarks;161
8.5.6;References;161
8.6;Dimensionality of Scores Obtained witha Paired-Comparison Tournament Systemof Questionnaire Items;163
8.6.1;1 Preference Data Collection;163
8.6.2;2 Preference Data;164
8.6.3;3 Scoring Algorithms;165
8.6.4;4 Dimensions in Preference Data;166
8.6.5;5 Conclusions;170
8.6.6;References;170
8.7;Using Rasch Measurement to Assess the Roleof the Traditional Family in Italy;171
8.7.1;1 Introduction;171
8.7.2;2 Data and Descriptive Analysis;172
8.7.3;3 Method;173
8.7.4;4 Discussion;175
8.7.5;References;177
8.8;Preserving the Clustering Structure by a Projection PursuitApproach;178
8.8.1;1 Introduction;178
8.8.2;2 Projection Pursuit for Preserving the Clustering Structure;180
8.8.2.1;2.1 The Critical Bandwidth to Test Multimodality;180
8.8.2.2;2.2 Projection Pursuit Using the Adjusted Critical Bandwidth;182
8.8.3;3 Numerical Results;182
8.8.3.1;3.1 A Simulation Study;182
8.8.3.2;3.2 Real Data Applications;183
8.8.4;4 Concluding Remarks;185
8.8.5;References;185
8.9;Association Rule Mining of Multimedia Content;186
8.9.1;1 Introduction;186
8.9.2;2 Syntactic Analysis of Video Data;187
8.9.3;3 Semantic Analysis;189
8.9.4;4 Association Rules;190
8.9.5;5 Synergy Effects;191
8.9.6;References;192
9;Part V Classification and Classification Tree;194
9.1;Automatic Dictionary- and Rule-Based Systems for Extracting Information from Text;195
9.1.1;1 Introduction;195
9.1.2;2 A Model for Creating a Meta-dictionary by Means of a Hybrid System;197
9.1.3;3 Application to the Istat TUS Survey;199
9.1.4;References;204
9.2;Several Computational Studies About Variable Selection for Probabilistic Bayesian Classifiers;205
9.2.1;1 Introduction;205
9.2.2;2 Bayesian Networks and Classification;206
9.2.2.1;2.1 Learning Bayesian Networks;207
9.2.2.2;2.2 Classifiers Based on Bayesian Networks;207
9.2.3;3 Feature Subset Selection for Classification;207
9.2.4;4 Experimental Results and Conclusions;208
9.2.5;References;212
9.3;Semantic Classification and Co-occurrences: A Methodfor the Rules Production for the Information Extractionfrom Textual Data;214
9.3.1;1 Introduction;214
9.3.2;2 The Analyzed Corpus and Semantic Classification;215
9.3.3;3 Rules Production Using Co-occurrences and Collocations;215
9.3.4;4 Future Developments and Improvements;220
9.3.5;References;221
9.4;The Effectiveness of University Education: A Structural Equation Model;222
9.4.1;1 Introduction;222
9.4.2;2 The Model;223
9.4.3;3 Main Results;226
9.4.4;4 Conclusions;228
9.4.5;References;229
9.5;Simultaneous Threshold Interaction Detection in BinaryClassification;230
9.5.1;1 Introduction;230
9.5.2;2 Modeling Interaction Effects in Regression Analysis;231
9.5.3;3 The Trunk Model;233
9.5.4;4 Empirical Evidence;234
9.5.5;5 Concluding Remarks;236
9.5.6;References;237
9.6;Detecting Subset of Classifiers for Multi-attribute ResponsePrediction;238
9.6.1;1 Introduction;238
9.6.2;2 The SASSC Algorithm;239
9.6.3;3 Analyzing the Letter Recognition Dataset;242
9.6.4;4 Concluding Remarks;244
9.6.5;References;245
9.7;Clustering Textual Data by Latent Dirichlet Allocation: Applications and Extensions to Hierarchical Data;246
9.7.1;1 Introduction;246
9.7.2;2 The LDA Model;247
9.7.3;3 Considering Documents Structure: An Extension of the LDA Model;249
9.7.4;4 Generalizing the Prior Enrichment: Collapsed Variational Bayes;251
9.7.5;5 Discussion;253
9.7.6;References;253
9.8;Multilevel Latent Class Models for Evaluation of Long-term Care Facilities;254
9.8.1;1 Introduction and Data;254
9.8.2;2 The Model;255
9.8.3;3 The Results;257
9.8.4;4 Conclusions;261
9.8.5;References;261
9.9;Author-Coauthor Social Networks and Emerging ScientificSubfields;262
9.9.1;1 Introduction;262
9.9.2;2 Distribution of Tie Strength;264
9.9.3;3 Distribution of Clique Size;266
9.9.4;4 Random Graph Model for Preferential Attachment;267
9.9.5;5 The Emergence of Scientific Subfields;268
9.9.6;6 The Network of Well-Established Scholars;270
9.9.7;7 Conclusions;272
9.9.8;References;273
10;Part VI Statistical Models;274
10.1;A Hierarchical Model for Time Dependent Multivariate Longitudinal Data;275
10.1.1;1 Introduction;275
10.1.2;2 Model-Based Approach to Three-Way Data Clustering;276
10.1.3;3 Multivariate Hidden Markov Model for Three-Way Data Clustering;277
10.1.4;4 Computational Details;279
10.1.5;5 Simulation Results;280
10.1.6;6 Conclusion;282
10.1.7;References;283
10.2;Covariate Error Bias Effects in Dynamic Regression Model Estimation and Improvement in the Prediction by Covariate Local Clusters;284
10.2.1;1 Introduction;284
10.2.2;2 Bias Effects in Dynamic Regression Models with Errors in the Covariate;285
10.2.3;3 A Local Cluster Kalman Filter;286
10.2.4;4 Simulation Experiments;287
10.2.5;5 An Application;289
10.2.6;6 Conclusions;291
10.2.7;References;291
10.3;Local Multilevel Modeling for Comparisons of InstitutionalPerformance;292
10.3.1;1 Introduction;292
10.3.2;2 Capturing Local Behaviour;293
10.3.2.1;2.1 Mixture Modeling;293
10.3.2.2;2.2 Cluster-Weighted Modeling;294
10.3.3;3 The Proposal: Local Multilevel Modeling;295
10.3.4;4 An Example;296
10.3.5;5 Conclusions and Further Research;298
10.3.6;References;299
10.4;Modelling Network Data: An Introduction to Exponential Random Graph Models;300
10.4.1;1 Introduction;300
10.4.2;2 Exponential Random Graph Models for Social Networks;301
10.4.3;3 The Collaboration Network of Italian Scholars on Population Studies;303
10.4.4;4 Model Estimation Results;305
10.4.5;5 Concluding Remarks;307
10.4.6;References;307
11;Part VII Latent Variables;309
11.1;An Analysis of Earthquakes Clustering Based on a Second-Order Diagnostic Approach;310
11.1.1;1 Introduction;310
11.1.2;2 Second-Order Residual Analysis;311
11.1.2.1;2.1 The Weighted Process and Its Second-Order Properties;311
11.1.2.1.1;2.1.1 The Weighted Spectrum;312
11.1.2.1.2;2.1.2 The Weighted Correlation Integral;312
11.1.3;3 Space-Time ETAS Model;313
11.1.4;4 Nonparametric Estimation and Diagnostics;314
11.1.5;5 Conclusion;317
11.1.6;References;317
11.2;Latent Regression in Rasch Framework;319
11.2.1;1 Introduction;319
11.2.2;2 Longitudinal Latent Regression Model;320
11.2.3;3 Latent Regression Rasch Model with Missing Data;322
11.2.4;4 Conclusion;325
11.2.5;References;326
11.3;A Multilevel Latent Variable Model for Multidimensional Longitudinal Data;328
11.3.1;1 Introduction;328
11.3.2;2 Model for Continuous Responses;329
11.3.2.1;2.1 Model Specification;329
11.3.2.2;2.2 Estimation;331
11.3.2.3;2.3 Application to a Real Data Set;333
11.3.3;3 Conclusion;335
11.3.4;References;335
11.4;Turning Point Detection Using Markov Switching Models with Latent Information;336
11.4.1;1 Introduction;336
11.4.2;2 The Generalized Hamilton Model with Latent Information;337
11.4.3;3 Identifying and Forecasting USA and Japanese Turning Points;339
11.4.4;References;343
12;Part VIII Knowledge Extraction from Temporal Data;344
12.1;Statistical and Numerical Algorithms for Time SeriesClassification;345
12.1.1;1 Introduction;345
12.1.2;2 A Simulation Experiment;348
12.1.3;3 Application to Real Data;350
12.1.4;4 Concluding Remarks;351
12.1.5;References;351
12.2;Mining Time Series Data: A Selective Survey;353
12.2.1;1 Introduction;353
12.2.2;2 Comparing Time Series Shape;354
12.2.3;3 Criteria Based on Fourier and Wavelet Analysis;355
12.2.4;4 Structural Dissimilarity;357
12.2.5;5 Final Remarks;358
12.2.6;References;359
12.3;Predictive Dynamic Models for SMEs;361
12.3.1;1 Introduction;361
12.3.2;2 Default Estimation: A Methodological Proposal;362
12.3.3;3 Data Sources;364
12.3.4;4 Application;364
12.3.5;5 Conclusion;365
12.3.6;References;366
12.4;Clustering Algorithms for Large Temporal Data Sets;367
12.4.1;1 The Framework: Temporal Data Mining;367
12.4.2;2 Temporal Cluster Analysis;368
12.4.3;3 Clustering Algorithms: Applicability to Large Temporal Datasets;370
12.4.4;4 An Example of Application on a Radar Satellite Data Base;372
12.4.5;5 Concluding Remarks;373
12.4.6;References;374
13;Part IX Outlier Detection and Robust Methods;376
13.1;Robust Clustering for Performance Evaluation;377
13.1.1;1 Introduction;377
13.1.2;2 Mahalanobis Distances and the Forward Search;378
13.1.3;3 Example;379
13.1.4;References;385
13.2;Outliers Detection Strategy for a Curve Clustering Algorithm;387
13.2.1;1 Introduction;387
13.2.2;2 Dynamical Curves Clustering with Free knots Spline Estimation;388
13.2.3;3 An Improvement of DCC&FSE Algorithm;389
13.2.4;4 The Outliers Selection Process;390
13.2.5;5 Main Results;391
13.2.6;6 Conclusion and Future Work;394
13.2.7;References;394
13.3;Robust Fuzzy Classification;395
13.3.1;1 Introduction;395
13.3.2;2 Robust Fuzzy Cluster Analysis;396
13.3.3;3 Confirmatory Analysis;398
13.3.4;References;402
13.4;Weighted Likelihood Inference for a Mixed Regressive Spatial Autoregressive Model;403
13.4.1;1 Introduction;403
13.4.2;2 A Weighted Likelihood Approach;404
13.4.3;3 A Small Simulation Study;406
13.4.4;4 A Real Example;408
13.4.5;5 Final Remarks;409
13.4.6;References;410
13.5;Detecting Price Outliers in European Trade Data with the Forward Search;411
13.5.1;1 Introduction;411
13.5.2;2 Application Context, Data and Statistical Patterns;412
13.5.3;3 Application of the Forward Search;413
13.5.4;4 Heuristic Comparison with the ``Backward'' Outliers;416
13.5.5;5 Towards an Automatic Procedure;416
13.5.6;6 Discussion and Main Conclusions;418
13.5.7;References;418
14;Part X Statistical Methods for Financial and Economics Data;420
14.1;Comparing Continuous Treatment Matching Methods in PolicyEvaluation;421
14.1.1;1 Introduction;421
14.1.2;2 Simulating a Subsidies Allocation Mechanism:The Case of L.488;422
14.1.3;3 The Matching Methods in the Continuous Framework;423
14.1.4;4 The Monte Carlo Experiment;424
14.1.5;5 Results and Conclusions;426
14.1.6;References;428
14.2;Temporal Aggregation and Closure of VARMA Models: Some New Results;429
14.2.1;1 Introduction;429
14.2.2;2 Temporal Aggregation;430
14.2.3;3 Temporal Aggregation and VARMA Models;431
14.2.4;4 ``Markovian" Representation;433
14.2.5;References;436
14.3;An Index for Ranking Financial Portfolios According to Internal Turnover;438
14.3.1;1 Introduction;438
14.3.2;2 Style Analysis Models;439
14.3.3;3 Ranking Portfolios According to Internal Turnover;441
14.3.4;4 Concluding Remarks;444
14.3.5;References;445
14.4;Bayesian Hidden Markov Modelsfor Financial Data;446
14.4.1;1 Introduction;446
14.4.2;2 The Model;447
14.4.3;3 Computational Implementation;448
14.4.4;4 Bayesian Inference and Forecasting;450
14.4.5;5 An Application to Financial Data;451
14.4.6;6 Conclusions;453
14.4.7;References;453
15;Part XI Missing Values;455
15.1;Regression Imputation for Space-Time Datasets with MissingValues;456
15.1.1;1 Introduction;456
15.1.2;2 A New Regression Single Imputation Method;458
15.1.3;3 Missing Data Simulation;459
15.1.4;4 Results;461
15.1.5;5 Toward a Multiple Imputation Method;461
15.1.6;References;463
15.2;A Multiple Imputation Approach in a Survey on University Teaching Evaluation;464
15.2.1;1 Introduction;464
15.2.2;2 An Imputation Procedure to Recover for Missingness;465
15.2.3;3 An Application to the Data on the Evaluationof University Teaching;466
15.2.3.1;3.1 AD;468
15.2.3.2;3.2 AE;468
15.2.4;4 Some Final Remarks;472
15.2.5;References;472mehr

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