Data analysis and applications 1 : clustering and regression, modeling-estimating, forecasting and data mining / edited by Christos H. Skiadas, James R. Bozeman.

Format:

Book

Contributor:

Skiadas, Christos H., editor.

Bozeman, James R., editor.

Series:

Innovation, entrepreneurship and management series. Big data, artificial intelligence and data analysis set ; 2.

THEi Wiley ebooks.

Big data, artificial intelligence and data analysis set ; 2

THEi Wiley ebooks

Language:

English

Subjects (All):

Quantitative research.

Physical Description:

1 online resource (xxix, 246 pages) : illustrations.

Edition:

1st edition

Place of Publication:

London, England ; Hoboken, New Jersey : ISTE : Wiley, 2019.

System Details:

Access using campus network via VPN at home (THEi Users Only).

text file

Summary:

"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 the field, who summarizes the developments in data analysis over the last 50 years. The book is then divided into three parts: Part 1 presents clustering and regression cases; Part 2 examines grouping and decomposition, GARCH and threshold models, structural equations, and SME modeling; and Part 3 presents symbolic data analysis, time series and multiple choice models, modeling in demography, and data mining."-- Provided by publisher.

Contents:

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. Challenges

I.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 shapes

1.3.11. 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 updating

2.2.3. 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 tool

4.3. 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

5.4. Grouping property of variance decomposition methods

5.4.1. Analysis of grouping property for CAR scores

5.4.2. Demonstration with two predictors

5.4.3. Analysis of grouping property using SVD

5.4.4. Application to the diabetes data set

5.5. Conclusions

5.6. References

6. On GARCH Models with Temporary Structural Changes

6.1. Introduction

6.2. The model

6.2.1. Trend model

6.2.2. Intervention GARCH model

6.3. Identification

6.4. Simulation

6.4.1. Simulation on trend model

6.4.2. Simulation on intervention trend model

6.5. Application

6.6. Concluding remarks

6.7. References.

7. A Note on the Linear Approximation of TAR Models

7.1. Introduction

7.2. Linear representations and linear approximations of nonlinear models

7.3. Linear approximation of the TAR model

7.4. References

8. An Approximation of Social Well-Being Evaluation Using Structural Equation Modeling

8.1. Introduction

8.2. Wellness

8.3. Social welfare

8.4. Methodology

8.5. Results

8.6. Discussion

8.7. Conclusions

8.8. References

9. An SEM Approach to Modeling Housing Values

9.1. Introduction

9.2. Data

9.3. Analysis

9.4. Conclusions

9.5. References

10. Evaluation of Stopping Criteria for Ranks in Solving Linear Systems

10.1. Introduction

10.2. Methods

10.2.1. Preliminaries

10.2.2. Iterative methods

10.3. Formulation of linear systems

10.4. Stopping criteria

10.5. Numerical experimentation of stopping criteria

10.5.1. Convergence of stopping criterion

10.5.2. Quantiles

10.5.3. Kendall correlation coefficient as stopping criterion

10.6. Conclusions

10.7. Acknowledgments

10.8. References

11. Estimation of a Two-Variable Second- Degree Polynomial via Sampling

11.1. Introduction

11.2. Proposed method

11.2.1. First restriction

11.2.2. Second restriction

11.2.3. Third restriction

11.2.4. Fourth restriction

11.2.5. Fifth restriction

11.2.6. Coefficient estimates

11.3. Experimental approaches

11.3.1. Experiment A

11.3.2. Experiment B

11.4. Conclusions

11.5. References

PART 3: Estimators, Forecasting and Data Mining

12. Displaying Empirical Distributions of Conditional Quantile Estimates: An Application of Symbolic Data Analysis to the Cost Allocation Problem in Agriculture

12.1. Conceptual framework and methodological aspects of cost allocation

12.2. The empirical model of specific production cost estimates.

12.3. The conditional quantile estimation

12.4. Symbolic analyses of the empirical distributions of specific costs

12.5. The visualization and the analysis of econometric results

12.6. Conclusion

12.7. Acknowledgments

12.8. References

13. Frost Prediction in Apple Orchards Based upon Time Series Models

13.1. Introduction

13.2. Weather database

13.3. ARIMA forecast model

13.3.1. Stationarity and differencing

13.3.2. Non-seasonal ARIMA models

13.4. Model building

13.4.1. ARIMA and LR models

13.4.2. Binary classification of the frost data

13.4.3. Training and test set

13.5. Evaluation

13.6. ARIMA model selection

13.7. Conclusions

13.8. Acknowledgments

13.9. References

14. Efficiency Evaluation of Multiple-Choice Questions and Exams

14.1. Introduction

14.2. Exam efficiency evaluation

14.2.1. Efficiency measures and efficiency weighted grades

14.2.2. Iterative execution

14.2.3. Postprocessing

14.3. Real-life experiments and results

14.4. Conclusions

14.5. References

15. Methods of Modeling and Estimation in Mortality

15.1. Introduction

15.2. The appearance of life tables

15.3. On the law of mortality

15.4. Mortality and health

15.5. An advanced health state function form

15.6. Epilogue

15.7. References

16. An Application of Data Mining Methods to the Analysis of Bank Customer Profitability and Buying Behavior

16.1. Introduction

16.2. Data set

16.3. Short-term forecasting of customer profitability

16.4. Churn prediction

16.5. Next-product-to-buy

16.6. Conclusions and future research

16.7. References

List of Authors

Index

Other titles from iSTE in Innovation, Entrepreneurship and Management

EULA.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781119597681

1119597684

9781119597568

1119597560

9781119597575

1119597579

OCLC:

1089986481