Data Analysis and Applications 1 : New and Classical Approaches.

"This series of books collects a diverse array of work that provides the reader with theoretical and applied information on data analysis methods, models, and techniques, along with appropriate applications. Volume 1 begins with an introductory chapter by Gilbert Saporta, a leading expert in th...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Skiadas, Christos
Otros Autores: Bozeman, James R.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2019.
Temas:
Quantitative research.
Forecasting.
Regression Analysis.
Data Mining.
Social Science > Future Studies.
Mathematics > Probability & Statistics > Regression Analysis.
Computers > Data Science > Data Analytics.
Recherche quantitative.
Quantitative research
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover; Half-Title Page; Title Page; Copyright Page; Contents; Preface; Introduction: 50 Years of Data Analysis: From Exploratory Data Analysis to Predictive Modeling and Machine Learning; I.1. The revolt against mathematical statistics; I.2. EDA and unsupervised methods for dimension reduction; I.2.1. The time of syntheses; I.2.2. The time of clusterwise methods; I.2.3. Extensions to new types of data; I.2.4. Nonlinear data analysis; I.2.5. The time of sparse methods; I.3. Predictive modeling; I.3.1. Paradigms and paradoxes; I.3.2. From statistical learning theory to empirical validation
  • I.3.3. ChallengesI. 4. Conclusion; I.5. References; PART 1: Clustering and Regression; 1. Cluster Validation by Measurement of Clustering Characteristics Relevant to the User; 1.1. Introduction; 1.2. General notation; 1.3. Aspects of cluster validity; 1.3.1. Small within-cluster dissimilarities; 1.3.2. Between-cluster separation; 1.3.3. Representation of objects by centroids; 1.3.4. Representation of dissimilarity structure by clustering; 1.3.5. Small within-cluster gaps; 1.3.6. Density modes and valleys; 1.3.7. Uniform within-cluster density; 1.3.8. Entropy; 1.3.9. Parsimony
  • 1.3.10. Similarity to homogeneous distributional shapes1.3.11. Stability; 1.3.12. Further Aspects; 1.4. Aggregation of indexes; 1.5. Random clusterings for calibrating indexes; 1.5.1. Stupid K-centroids clustering; 1.5.2. Stupid nearest neighbors clustering; 1.5.3. Calibration; 1.6. Examples; 1.6.1. Artificial data set; 1.6.2. Tetragonula bees data; 1.7. Conclusion; 1.8. Acknowledgment; 1.9. References; 2. Histogram-Based Clustering of Sensor Network Data; 2.1. Introduction; 2.2. Time series data stream clustering; 2.2.1. Local clustering of histogram data
  • 2.2.2. Online proximity matrix updating2.2.3. Off-line partitioning through the dynamic clustering algorithm for dissimilarity tables; 2.3. Results on real data; 2.4. Conclusions; 2.5. References; 3. The Flexible Beta Regression Model; 3.1. Introduction; 3.2. The FB distribution; 3.2.1. The beta distribution; 3.2.2. The FB distribution; 3.2.3. Reparameterization of the FB; 3.3. The FB regression model; 3.4. Bayesian inference; 3.5. Illustrative application; 3.6. Conclusion; 3.7. References; 4. S-weighted Instrumental Variables; 4.1. Summarizing the previous relevant results
  • 4.2. The notations, framework, conditions and main tool4.3. S-weighted estimator and its consistency; 4.4. S-weighted instrumental variables and their consistency; 4.5. Patterns of results of simulations; 4.5.1. Generating the data; 4.5.2. Reporting the results; 4.6. Acknowledgment; 4.7. References; PART 2: Models and Modeling; 5. Grouping Property and Decomposition of Explained Variance in Linear Regression; 5.1. Introduction; 5.2. CAR scores; 5.2.1. Definition and estimators; 5.2.2. Historical criticism of the CAR scores; 5.3. Variance decomposition methods and SVD

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