Classification and Multivariate Analysis for Complex Data Structures

Table of Contents

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   Book Overview
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   Chapter 1 Principal Component Analysis for Categorical Histogram Data: Some Open Directions of Research
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   Chapter 2 Factorial Conjoint Analysis Based Methodologies
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   Chapter 3 Ordering and Scaling Objects in Multivariate Data Under Nonlinear Transformations of Variables
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   Chapter 4 Statistical Models to Predict Academic Churn Risk
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   Chapter 5 The Poisson Processes in Cluster Analysis
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   Chapter 6 TWO-CLASS Trees for Non-Parametric Regression Analysis
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   Chapter 7 Efficient Incorporation of Additional Information to Classification Rules
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   Chapter 8 The Choice of the Parameter Values in a Multivariate Model of a Second Order Surface with Heteroscedastic Error
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    Chapter 9 Mixed Mode Data Clustering: An Approach Based on Tetrachoric Correlations
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    Chapter 10 Optimal Scaling Trees for Three-Way Data
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    Chapter 11 A Study on Text Modelling via Dirichlet Compound Multinomial
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    Chapter 12 Automatic Multilevel Thresholding Based on a Fuzzy Entropy Measure
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    Chapter 13 Some Developments in Forward Search Clustering
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    Chapter 14 Spectral Graph Theory Tools for Social Network Comparison
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    Chapter 15 Improving the MHIST-p Algorithm for Multivariate Histograms of Continuous Data
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    Chapter 16 On Building and Visualizing Proximity Graphs for Large Data Sets with Artificial Ants
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    Chapter 17 Including Empirical Prior Information in Test Administration
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    Chapter 18 Italian Firms’ Geographical Location in High-tech Industries: A Robust Analysis
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    Chapter 19 Robust Tests for Pareto Density Estimation
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    Chapter 20 Bootstrap and Nonparametric Predictors to Impute Missing Data
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    Chapter 21 On the Use of Boosting Procedures to Predict the Risk of Default
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    Chapter 22 Assessing Similarity of Rating Distributions by Kullback-Leibler Divergence
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    Chapter 23 Sector Classification in Stock Markets: A Latent Class Approach
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    Chapter 24 Partitioning the Geometric Variability in Multivariate Analysis and Contingency Tables
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    Chapter 25 One-Dimensional Preference Data Imputation Through Transition Rules
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    Chapter 26 About a Type of Quasi Linear Estimating Equation Approach
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    Chapter 27 Causal Inference Through Principal Stratification: A Special Type of Latent Class Modelling
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    Chapter 28 Scaling the Latent Variable Cultural Capital via Item Response Models and Latent Class Analysis
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    Chapter 29 Assessment of Latent Class Detection in PLS Path Modeling: a Simulation Study to Evaluate the Group Quality Index performance
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    Chapter 30 Non-Linear Relationships in SEM with Latent Variables: Some Theoretical Remarks and a Case Study
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    Chapter 31 Multidimensional Scaling Versus Multiple Correspondence Analysis When Analyzing Categorization Data
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    Chapter 32 Multidimensional Scaling as Visualization Tool of Web Sequence Rules
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    Chapter 33 Partial Compliance, Effect of Treatment on the Treated and Instrumental Variables
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    Chapter 34 Method of Quantification for Qualitative Variables and their Use in the Structural Equations Models
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    Chapter 35 Monitoring Panel Performance Within and Between Sensory Experiments by Multi-Way Analysis
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    Chapter 36 A Proposal for Handling Categorical Predictors in PLS Regression Framework
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    Chapter 37 On the Use of Archetypes and Interval Coding in Sensory Analysis
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    Chapter 38 From Histogram Data to Model Data Analysis
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    Chapter 39 Use of Genetic Algorithms When Computing Variance of Interval Data
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    Chapter 40 Spatial Visualization of Conceptual Data
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    Chapter 41 A Test of LBO Firms’ Acquisition Rationale: The French Case
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    Chapter 42 Kernel Intensity for Space-Time Point Processes with Application to Seismological Problems
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    Chapter 43 Summarizing and Mining Streaming Data via a Functional Data Approach
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    Chapter 44 Clustering Complex Time Series Databases
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    Chapter 45 Use of a Flexible Weight Matrix in a Local Spatial Statistic
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    Chapter 46 Constrained Variable Clustering and the Best Basis Problem in Functional Data Analysis
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    Chapter 47 Plaid Model for Microarray Data: an Enhancement of the Pruning Step
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    Chapter 48 Classification of the Human Papilloma Viruses
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    Chapter 49 Toward the Discovery of Itemsets with Significant Variations in Gene Expression Matrices