Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data (4th printing) - Bruce Ratner, Ph.D.
Table of Contents
ARTICLES for the Second Edition: See Volumes 8 - 11
Highlight of the Book
The major significance and special interest is in Chapter 15, where I talk about statistics and machine learning, and I define that statistical learning is the ability to solve statistical problems using non-statistical machine learning. GenIQ is now presented as such a non-statistical machine learning model. Moreover, in Chapter 16 GenIQ serves as an effective method for finding the best possible subset of variables for a model.- from the original and newly genetically data mined variables. The later suggests that GenIQ is a data mining tool par excellence.
The following article uses a novel dataset, yet sets forth for the attention of the data analyst's mind and visual acuity of GenIQ's method and application in the context of Chapters 15 and 16.
The following links serve to swank GenIQ in its own right, as a non-statistical method solving the standard statistical problems of classification and prediction by uniquely unfolding the task of optimizing the decile table (equivalently the ROC curve, and the K-S statistic), and as a very powerful data mining tool.
a] The GenIQ Model Software: Benefits
b] The GenIQ Model: A Unique Data Mining Tool
c] The GenIQ Model: Model Building Illustration - "Let's Play"
d] The GenIQ Model: Model Building Illustration - "Survival Analysis"
e] The GenIQ Model Model Building Illustration - "Phat Example"
f ] The GenIQ Model: When Statistical Models "Go Oft Astray"
g] The GenIQ Model: When Data Are Not Straight
h] The GenIQ Model: A 9-step Modeling Process
i] The GenIQ Model: FAQs
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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.
Annotation ©2003 Book News, Inc. Review
A consultant to the database marketing industry describes specific methods for solving the most commonly experienced problems encountered in the field. He also provides a background by discussing the basic methodologies of database analysis and modeling. Among the problems he addresses are variable assessment, response modeling, profit modeling, and market segment classification modeling.
As the Preface (p. iii) begins, “This book is a compilation of essays.” That is the only negative comment that I have about this book, which is remarkable if one considers the tone of the comments that I have made in the various reports that I have served up in these pages for a host of unexceptional data mining books. For one thing, this book is not at all focused on the myriad of data processing steps that data mining practitioners undertake once their datasets are available. For another thing, the essays in this book were written by a statistician. Subtitled “Effective Techniques for Mining Big Data,” the book focuses strictly on modeling with a designated dependent variable. Also, the author builds a historical basis for data mining that includes EDA as a significant catalyst — a perspective that should interest most statisticians.
Stanford Gradute School of Business, On Booklist
Georgetown University Lauinger Library
Monash University, On Syllabus for Business & Economics SMC.edu Library, On Statistical Reading List
Oklahoma State University, Reference Text for Graduate Class
Statistical Modeling and Analysis for Database Marketing delivers a collection of successful database marketing methodologies for big data. It also delivers a thorough analysis of these cutting-edge techniques, which include non-statistical machine learning and genetic intelligent hybrid models.
Hi Bruce, Just read your book. It's the BEST manual of Data Mining I have ever read. You"ll make us redudnat :). I am referring it to everyone I know.
Regards, Arup Guha.
An essential book for statistical analysts building predictive models for database marketing. This is a must have introductory book for the practitioner using data mining to build predictive models in industry. While it does have a few snippets of SAS code, it is a conceptual book that explains the "why" and the "how" of practical model building. It dispenses with the antiquated notion of the "true" model of classical statistics and econometrics, and shows how to arrive at an acceptable model that yeilds good predictions. As practitioner's, this is what we care about most. Among other things, it gives good explanations of: (1) the EDA paradigm versus classical statistics (2) Tukey's bulging rule for transforming variables (3) variable selection. It discusses some of the weaknesses of automatic variable selection methods (4) smoothed scatterplots and logit plots (5) decile analysis and using bootstrapping to derive confidence intervals for cum lift.
The book shows you how to use logistic regression, OLS, and CHAID to build predictive models. For those interested in Genetic modeling, it has a clearly written chapter on the subject that explains how genetic modeling can be used to create new variables that can have more information than either of the original variables.
I predict that Dr. Ratner's Statistical Modeling and Analysis for Database Marketers: Effective Techniques for Mining Big Data will be on every database marketer's bookshelf. Dr Ratner has put together an assembly of chapters that provide an indispensable resource for the daily problems facing data analysts and model builders in the database/direct marketing community. In each of the seveenteen chatpers Dr. Ratner addresses a typical problem and discusses the common solution. He points out unknown working assumptions or weaknesses of the latter, and then offers better solutions, which require basic knowledge of EDA/data mining. Dr. Ratner's writing style is unique as he makes familar concepts new, and new concepts familar. Thus, the book is easy and enjoyable reading. I specially like chapter that blends statistics with the machine learning, such as the introduction of the GenIQ Model.
I consider myself fortunate to be the first to review this book. The title aptly indicates what the book is about: Statistical Modeling and Analysis for Database Marketers: Effective Techniques for Mining Big Data. The author provides in a Tukey-esque manner a collection of solutions to common problems facing database analysts, model builders, and marketers. The book can uniquely serve as a textbook, a how-to guide, and a reference source depending on the reader's statistical training and database marketing experience. Moreover, the author actually goes where other authors provide lip service: he creates the marriage of the "old" statistical methodologies with the new machine learning influence by introducing machine learning methods specifically tailored to database assessment of optimal model performance. The book's illustrations involve real problems, real data, and better solutions. This book is a keeper!
I work for an insurance company in a small department of statisticians (5 people), and we usually bring in text books, articles, presentation copies for periodical discussions. I brought in a book just purchased to discuss with my colleagues -your excellent book - Statistical Modeling and Analysis for Database Marketing.
Dr. Ratner should be commended for writing this book that is truly original. As far as I am concerned there is no other book of its kind: most books present old topics and new subject matter in various forms without real change or improvement in substance. Each of Ratner's chapters is unique: a common problem is presented and a novel, superior solution (over the typical solution approach) is discussed. And, new subject matter is presented with an orignal and viable solution. Dr. Ratner shares his knowledge and has a lot of it to go around.
Bruce - great book! Where can I get the data set used in your book Statistical Modeling and Analysis for Database Marketing? Thanks.
I would like to say that I found your most recent book - Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data - well written and easy to understand.
Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data provides an excellent treatment of statistical modeling for database marketers. The book explains clearly how statistical modeling should be done in practice. In particular, I can see where the GenIQ Model can come handy in subselection of predictors and smoothing the variables. The book is a must for every statistical database marketing analyst and model builder.
I enjoyed reading your book Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data. And, I just found your website DMSTAT1: Great collection of articles/reports!!
Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data is an excellent book on statistical modeling relevant to marketing. 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.
Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data is quite a good book, and without "competition" in the sense that there aren't many out there that are so focused and so accessible. Excellent job.
Fortunately, I discovered your book Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data. It finally arrived: It contains tons of useful information.
I was browsing the web for a statistics data mining book.Your book Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data was the top citation/link. After previewing your book's contents on your website, I forthwith ordered it. It should arrive tomorrow - I can't wait. I know it's going to be priceless. Thanks!
- Hardcover: 400 pages
- Publisher: CRC Press, June 2003
- Third Printing, June 2005
- ISBN: 1574443445
- Number of Characters: 633,451
- Number of Words: 86,254
- Number of Sentences: 8,974
- Words per Sentence: 9.6
- Words per Ounce: 3,642
Call Bruce Ratner at 516.791.3544, or firstname.lastname@example.org
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