Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data

Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data

Traditional statistical methods are limited in their ability to meet the modern challenge of mining large amounts of data. Data miners, analysts, and statisticians are searching for innovative new data mining techniques with greater predictive power, an attribute critical for reliable models and ana...

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Tác giả chính: Ratner, Bruce
Định dạng: Sách
Ngôn ngữ:English
Được phát hành: CRC Press 2009
Truy cập trực tuyến:http://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/1350
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Thư viện lưu trữ: Thư viện Trường Đại học Đà Lạt
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institution Thư viện Trường Đại học Đà Lạt
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language English
description Traditional statistical methods are limited in their ability to meet the modern challenge of mining large amounts of data. Data miners, analysts, and statisticians are searching for innovative new data mining techniques with greater predictive power, an attribute critical for reliable models and analyses. Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data delivers a collection of successful database marketing methodologies for big data. This compendium solves common database marketing problems by applying new hybrid modeling techniques that combine traditional statistical and new machine learning methods. The book delivers a thorough analysis of these cutting-edge techniques, which include non-statistical machine learning and genetic intelligent hybrid models. By following the step-by-step procedures detailed in the text, database marketing professionals can learn how to apply the proper statistical techniques to any database marketing challenge. The practical case studies and examples provided involve real problems and real data, and are taken from a variety of industries, including banking, insurance, finance, retail, and telecommunications.
format Book
author Ratner, Bruce
spellingShingle Ratner, Bruce
Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data
author_facet Ratner, Bruce
author_sort Ratner, Bruce
title Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data
title_short Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data
title_full Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data
title_fullStr Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data
title_full_unstemmed Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data
title_sort statistical modeling and analysis for database marketing: effective techniques for mining big data
publisher CRC Press
publishDate 2009
url http://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/1350
_version_ 1757671552712704000
spelling oai:scholar.dlu.edu.vn:DLU123456789-13502009-11-27T09:04:22Z Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data Ratner, Bruce Traditional statistical methods are limited in their ability to meet the modern challenge of mining large amounts of data. Data miners, analysts, and statisticians are searching for innovative new data mining techniques with greater predictive power, an attribute critical for reliable models and analyses. Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data delivers a collection of successful database marketing methodologies for big data. This compendium solves common database marketing problems by applying new hybrid modeling techniques that combine traditional statistical and new machine learning methods. The book delivers a thorough analysis of these cutting-edge techniques, which include non-statistical machine learning and genetic intelligent hybrid models. By following the step-by-step procedures detailed in the text, database marketing professionals can learn how to apply the proper statistical techniques to any database marketing challenge. The practical case studies and examples provided involve real problems and real data, and are taken from a variety of industries, including banking, insurance, finance, retail, and telecommunications. Introduction * The Personal Computer and Statistics * Statistics and Data Analysis * EDA * The EDA Paradigm * EDA Weaknesses * Small and Big Data * Data Mining Paradigm * Statistics and Machine Learning * Statistical Learning Two Simple Data Mining Methods for Variable Assessment * Correlation Coefficient * Scatterplots * Data Mining * Smoothed Scatterplot * General Association Test * Summary Logistic Regression: The Workhorse of Database Response Modeling * Logistic Regression Model * Case Study * Logits and Logit Plots * The Importance of Straight Data * Re-expressing for Straight Data * Straight Data for Case Study * Techniques When Bulging Rule Does Not Apply * Re-expressing MOS_OPEN * Assessing the Importance of Variables * Important Variables for Case Study * Relative Importance of the Variables * Best Subset of Variables for Case Study * Visual Indicators of Goodness of Model Predictions * Evaluating the Data Mining Work * Smoothing a Categorical Variable * Additional Data Mining Work for Case Study * Summary Ordinary Regression: The Workhorse of Database Profit Modeling * Ordinary Regression Model * Illustration * Mini Case Study * Important Variables for Mini Case Study * Best Subset of Variable for Case Study * Summary CHAID for Interpreting a Logistic Regression Model * Logistic Regression Model * Database Marketing Response Model Case Study * CHAID * Multivariable CHAID Trees * CHAID Market Segmentation * CHAID Tree Graphs * Summary The Importance of the Regression Coefficient * The Ordinary Regression Model * Four Questions * Important Predictor Variables * P-values and BIG Data * Returning to Question #1 * Predictor Variable's Effect On Prediction * The Caveat * Returning to Question #2 * Ranking Predictor Variables By Effect On Prediction * Returning to Question #3 * Returning to Question #4 * Summary The Predictive Contribution Coefficient: A Measure of Predictive Importance * Background * Illustration of Decision Rule, Predictive Contribution Coefficient * Calculation of Predictive Contribution Coefficient * Extra Illustration of Predictive Contribution Coefficient * Summary CHAID for Specifying a Model with Interaction Variables * Interaction Variables * Strategy for Modeling with Interaction Variables * Strategy Based on the Notion of a Special Point * Example of a Response Model with an Interaction Variable * CHAID for Uncovering Relationships * Illustration of CHAID Specifying a Model * An Exploratory Look * Database Implication * Summary Market Segment Classification Modeling with Logistic Regression * Binary Logistic Regression * Polychotomous Logistic Regression Model * Model Building With PLR * Market Segmentation Classification Model * Summary CHAID as a Method for Filling in Missing Values * Introduction to the Problem of Missing Data * Missing-data Assumption * CHAID Imputation * Illustration * CHAID Most-likely Category Imputation for a Categorical Variable * Summary Identifying Your Best Customers: Descriptive, Predictive and Look-alike Profiling * Some Definitions * Illustration of a Flawed Targeting Effort * Well-Defined Targeting Effort * Predictive Profiles * Continuous Trees * Look-alike Profiling * Look-alike Tree Characteristics * Summary Assessment of Database Marketing Models * Accuracy for Response Model * Accuracy for Profit Model * Decile Analysis and Cum Lift for Response Model * Decile Analysis and Cum Lift for Profit Model * Precision for Response Model * Construction of SWMAD * Separability for Response and Profit Models * Guidelines for Using Cum Lift, HL/SWMAD and CV * Summary Bootstrapping in Database Marketing: A New Approach for Validating Models * Traditional Model Validation * Illustration * Three Questions * The Bootstrap * How to Bootstrap * Bootstrap Decile Analysis Validation * Another Question * Bootstrap Assessment of Model Implementation Performance * Bootstrap Assessment of Model Efficiency * Summary Visualization of Database Models * Brief History of the Graph * Star Graph Basics * Star Graphs for Single Variables * Star Graphs for Many Variables Considered Jointly * Profile Curves Method * Illustration * Summary SAS Code for Star Graphs for Each Demographic Variable about the Deciles SAS Code for Star Graphs for Each Decile About the Demographic Variables * SAS Code for Profile Curves: All Deciles * Reference * Genetic Modeling in Database Marketing: The GenIQ Model * What Is Optimization? * What Is Genetic Modeling * Genetic Modeling: An Illustration * Parameters for Controlling a Genetic Model Run * Genetic Modeling: Strengths and Limitations * Goals of Modeling in Database Marketing * The GenIQ Response Model * The GenIQ Profit Model * Case Study-Response Model * Case Study-Profit Model * Summary Finding the Best Variables for Database Marketing Models * Background * Weakness in the Variable Selection Methods * Goals of Modeling in Database Marketing * Variable Selection with GenIQ * Nonlinear Alternative to Logistic Regression Model * Summary Interpretation of Coefficient-free Models * The Linear Regression Coefficient * Illustration for the Simple Ordinary Regression Model * The Quasi-Regression Coefficient for Simple Regression Models * Partial Quasi-RC for the Everymodel * Quasi-RC for a Coefficient-free Model * Summary 2009-11-27T09:04:22Z 2009-11-27T09:04:22Z 2003 Book http://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/1350 en application/octet-stream CRC Press

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