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Statistical and machine-learning data mining : techniques for better predictive modeling and analysis of big data / / Bruce Ratner
Statistical and machine-learning data mining : techniques for better predictive modeling and analysis of big data / / Bruce Ratner
Autore Ratner Bruce
Edizione [2nd ed.]
Pubbl/distr/stampa Boca Raton : , : Taylor & Francis, , 2012
Descrizione fisica 1 online resource (524 p.)
Disciplina 658.8/72
Altri autori (Persone) RatnerBruce
Soggetto topico Database marketing - Statistical methods
Data mining - Statistical methods
ISBN 0-429-24862-8
1-4665-5121-6
1-280-12244-7
9786613526304
1-4398-6092-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Cover; Dedication; Contents; Preface; Acknowledgments; About the Author; 1. Introduction; 2. Two Basic Data Mining Methods for Variable Assessment; 3. CHAID-Based Data Mining for Paired-Variable Assessment; 4. The Importance of Straight Data: Simplicity and Desirability for Good Model-Building Practice; 5. Symmetrizing Ranked Data: A Statistical Data Mining Method for Improving the Predictive Power of Data; 6. Principal Component Analysis: A Statistical Data Mining Method for Many-Variable Assessment; 7. The Correlation Coefficient: Its Values Range between Plus/Minus 1, or Do They?
8. Logistic Regression: The Workhorse of Response Modeling9. Ordinary Regression: The Workhorse of Profit Modeling; 10. Variable Selection Methods in Regression: Ignorable Problem, Notable Solution; 11. CHAID for Interpreting a Logistic Regression Model; 12. The Importance of the Regression Coefficient; 13. The Average Correlation: A Statistical Data Mining Measure for Assessment of Competing Predictive Models and the Importance of the Predictor Variables; 14. CHAID for Specifying a Model with Interaction Variables; 15. Market Segmentation Classification Modeling with Logistic Regression
16. CHAID as a Method for Filling in Missing Values17. Identifying Your Best Customers: Descriptive, Predictive, and Look-Alike Profiling; 18. Assessment of Marketing Models; 19. Bootstrapping in Marketing: A New Approach for Validating Models; 20. Validating the Logistic Regression Model: Try Bootstrappin; 21. Visualization of Marketing ModelsData Mining to Uncover Innards of a Model; 22. The Predictive Contribution Coefficient: A Measure of Predictive Importance; 23. Regression Modeling Involves Art, Science, and Poetry, Too; 24. Genetic and Statistic Regression Models: A Comparison
25. Data Reuse: A Powerful Data Mining Effect of the GenIQ Model26. A Data Mining Method for Moderating Outliers Instead of Discarding Them; 27. Overfitting: Old Problem, New Solution; 28. The Importance of Straight Data: Revisited; 29. The GenIQ Model: Its Definition and an Application; 30. Finding the Best Variables for Marketing Models; 31. Interpretation of Coefficient-Free Models
Record Nr. UNINA-9910778813003321
Ratner Bruce  
Boca Raton : , : Taylor & Francis, , 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Statistical and machine-learning data mining : techniques for better predictive modeling and analysis of big data / / Bruce Ratner
Statistical and machine-learning data mining : techniques for better predictive modeling and analysis of big data / / Bruce Ratner
Autore Ratner Bruce
Edizione [2nd ed.]
Pubbl/distr/stampa Boca Raton : , : Taylor & Francis, , 2012
Descrizione fisica 1 online resource (524 p.)
Disciplina 658.8/72
Altri autori (Persone) RatnerBruce
Soggetto topico Database marketing - Statistical methods
Data mining - Statistical methods
ISBN 0-429-24862-8
1-4665-5121-6
1-280-12244-7
9786613526304
1-4398-6092-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Cover; Dedication; Contents; Preface; Acknowledgments; About the Author; 1. Introduction; 2. Two Basic Data Mining Methods for Variable Assessment; 3. CHAID-Based Data Mining for Paired-Variable Assessment; 4. The Importance of Straight Data: Simplicity and Desirability for Good Model-Building Practice; 5. Symmetrizing Ranked Data: A Statistical Data Mining Method for Improving the Predictive Power of Data; 6. Principal Component Analysis: A Statistical Data Mining Method for Many-Variable Assessment; 7. The Correlation Coefficient: Its Values Range between Plus/Minus 1, or Do They?
8. Logistic Regression: The Workhorse of Response Modeling9. Ordinary Regression: The Workhorse of Profit Modeling; 10. Variable Selection Methods in Regression: Ignorable Problem, Notable Solution; 11. CHAID for Interpreting a Logistic Regression Model; 12. The Importance of the Regression Coefficient; 13. The Average Correlation: A Statistical Data Mining Measure for Assessment of Competing Predictive Models and the Importance of the Predictor Variables; 14. CHAID for Specifying a Model with Interaction Variables; 15. Market Segmentation Classification Modeling with Logistic Regression
16. CHAID as a Method for Filling in Missing Values17. Identifying Your Best Customers: Descriptive, Predictive, and Look-Alike Profiling; 18. Assessment of Marketing Models; 19. Bootstrapping in Marketing: A New Approach for Validating Models; 20. Validating the Logistic Regression Model: Try Bootstrappin; 21. Visualization of Marketing ModelsData Mining to Uncover Innards of a Model; 22. The Predictive Contribution Coefficient: A Measure of Predictive Importance; 23. Regression Modeling Involves Art, Science, and Poetry, Too; 24. Genetic and Statistic Regression Models: A Comparison
25. Data Reuse: A Powerful Data Mining Effect of the GenIQ Model26. A Data Mining Method for Moderating Outliers Instead of Discarding Them; 27. Overfitting: Old Problem, New Solution; 28. The Importance of Straight Data: Revisited; 29. The GenIQ Model: Its Definition and an Application; 30. Finding the Best Variables for Marketing Models; 31. Interpretation of Coefficient-Free Models
Record Nr. UNINA-9910821503903321
Ratner Bruce  
Boca Raton : , : Taylor & Francis, , 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Statistical learning for big dependent data / / Daniel Peña, Ruey S. Tsay
Statistical learning for big dependent data / / Daniel Peña, Ruey S. Tsay
Autore Peña Daniel <1948->
Edizione [First edition.]
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , [2021]
Descrizione fisica 1 online resource (563 pages)
Disciplina 005.7
Collana Wiley series in probability and statistics
Soggetto topico Big data - Mathematics
Time-series analysis
Data mining - Statistical methods
Forecasting - Statistical methods
ISBN 1-119-41741-4
1-119-41740-6
1-119-41739-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction to big dependent data -- Linear univariate time series -- Analysis of multivariate time series -- Handling heterogeneity in many time series -- Clustering and classification of time series -- Dynamic factor models -- Forecasting with big dependent data -- Machine learning of big dependent data -- Spatio-temporal dependent data.
Record Nr. UNINA-9910829984503321
Peña Daniel <1948->  
Hoboken, New Jersey : , : Wiley, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
La Théorie de l'analyse Statistique Implicative Ou l'invraisemblance du Faux / / Régis Gras
La Théorie de l'analyse Statistique Implicative Ou l'invraisemblance du Faux / / Régis Gras
Autore Gras Régis
Pubbl/distr/stampa Toulouse : , : Cepadues Editions, , [2018]
Descrizione fisica 1 online resource (232 pages)
Disciplina 006.312
Soggetto topico Data mining - Statistical methods
Soggetto genere / forma Electronic books.
ISBN 2-36493-700-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione fre
Record Nr. UNINA-9910467535003321
Gras Régis  
Toulouse : , : Cepadues Editions, , [2018]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
La Théorie de l'analyse Statistique Implicative Ou l'invraisemblance du Faux / / Régis Gras
La Théorie de l'analyse Statistique Implicative Ou l'invraisemblance du Faux / / Régis Gras
Autore Gras Régis
Pubbl/distr/stampa Toulouse : , : Cepadues Editions, , [2018]
Descrizione fisica 1 online resource (232 pages)
Disciplina 006.312
Soggetto topico Data mining - Statistical methods
ISBN 2-36493-700-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione fre
Record Nr. UNINA-9910793306703321
Gras Régis  
Toulouse : , : Cepadues Editions, , [2018]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
La Théorie de l'analyse Statistique Implicative Ou l'invraisemblance du Faux / / Régis Gras
La Théorie de l'analyse Statistique Implicative Ou l'invraisemblance du Faux / / Régis Gras
Autore Gras Régis
Pubbl/distr/stampa Toulouse : , : Cepadues Editions, , [2018]
Descrizione fisica 1 online resource (232 pages)
Disciplina 006.312
Soggetto topico Data mining - Statistical methods
ISBN 2-36493-700-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione fre
Record Nr. UNINA-9910819905503321
Gras Régis  
Toulouse : , : Cepadues Editions, , [2018]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
WSDM '15 : proceedings of the Eighth ACM International Conference on Web Search and Data Mining : Jan. 31-Feb. 6, 2015, Shanghai, China / / edited by Xueqi Cheng [and three others]
WSDM '15 : proceedings of the Eighth ACM International Conference on Web Search and Data Mining : Jan. 31-Feb. 6, 2015, Shanghai, China / / edited by Xueqi Cheng [and three others]
Pubbl/distr/stampa New York : , : ACM, , 2015
Descrizione fisica 1 online resource (466 pages)
Disciplina 006.312
Soggetto topico Data mining
Data mining - Statistical methods
ISBN 1-4503-3317-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910375747403321
New York : , : ACM, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui