Modeling Online Auctions [NOOK Book]

( 1 )

Overview

Explore cutting-edge statistical methodologies for collecting, analyzing, and modeling online auction data

Online auctions are an increasingly important marketplace, as the new mechanisms and formats underlying these auctions have enabled the capturing and recording of large amounts of bidding data that are used to make important business decisions. As a result, new statistical ideas and innovation are needed to understand bidders, sellers, and prices. Combining methodologies ...

See more details below
Modeling Online Auctions

Available on NOOK devices and apps  
  • Nook Devices
  • NOOK HD/HD+ Tablet
  • NOOK
  • NOOK Color
  • NOOK Tablet
  • Tablet/Phone
  • NOOK for Windows 8 Tablet
  • NOOK for iOS
  • NOOK for Android
  • NOOK Kids for iPad
  • PC/Mac
  • NOOK for Windows 8
  • NOOK for PC
  • NOOK for Mac
  • NOOK Study
  • NOOK for Web

Want a NOOK? Explore Now

NOOK Book (eBook)
$70.68
BN.com price
(Save 43%)$124.00 List Price
Note: This NOOK Book can be purchased in bulk. Please email us for more information.

More About This Book

Overview

Explore cutting-edge statistical methodologies for collecting, analyzing, and modeling online auction data

Online auctions are an increasingly important marketplace, as the new mechanisms and formats underlying these auctions have enabled the capturing and recording of large amounts of bidding data that are used to make important business decisions. As a result, new statistical ideas and innovation are needed to understand bidders, sellers, and prices. Combining methodologies from the fields of statistics, data mining, information systems, and economics, Modeling Online Auctions introduces a new approach to identifying obstacles and asking new questions using online auction data.

The authors draw upon their extensive experience to introduce the latest methods for extracting new knowledge from online auction data. Rather than approach the topic from the traditional game-theoretic perspective, the book treats the online auction mechanism as a data generator, outlining methods to collect, explore, model, and forecast data. Topics covered include:

  • Data collection methods for online auctions and related issues that arise in drawing data samples from a Web site
  • Models for bidder and bid arrivals, treating the different approaches for exploring bidder-seller networks
  • Data exploration, such as integration of time series and cross-sectional information; curve clustering; semi-continuous data structures; and data hierarchies
  • The use of functional regression as well as functional differential equation models, spatial models, and stochastic models for capturing relationships in auction data
  • Specialized methods and models for forecasting auction prices and their applications in automated bidding decision rule systems

Throughout the book, R and MATLAB software are used for illustrating the discussed techniques. In addition, a related Web site features many of the book's datasets and R and MATLAB code that allow readers to replicate the analyses and learn new methods to apply to their own research.

Modeling Online Auctions is a valuable book for graduate-level courses on data mining and applied regression analysis. It is also a one-of-a-kind reference for researchers in the fields of statistics, information systems, business, and marketing who work with electronic data and are looking for new approaches for understanding online auctions and processes.

Visit this book's companion website by clicking here

Read More Show Less

Editorial Reviews

From the Publisher

"Modeling online auctions is a valuable book for graduate-level courses on data mining and applied regression analysis." (Mathematical Reviews, 2011)

"The referred volume offers a detailed survey of methods and run of online auctions by means of detailed analysis of empirical and statistical data . . . the volume is completed by a rich bibliography and index." (Zentralblatt Math, 2010)

"Modeling Online Auctions is a valuable book for graduate-level courses on data mining and applied regression analysis. It is also a one-of-a-kind reference for researchers in the fields of statistics, information systems, business, and marketing who work with electronic data and are looking for new approaches for understanding online auctions and processes". (Dublin Business Wire, 27 October 2010)

Read More Show Less

Product Details

  • ISBN-13: 9781118031865
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 12/1/2010
  • Series: Statistics in Practice , #91
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 1
  • Pages: 319
  • File size: 7 MB

Meet the Author

WOLFGANG JANK, PhD, is Associate Professor of Management Science and Statistics in the Robert H. Smith School of Business at the University of Maryland, where he is also Director of the Center for Complexity in Business. He has published over seventy articles on statistics and data mining in electronic commerce, marketing, information systems, and operations management. Dr. Jank is the coauthor of Statistical Methods in e-Commerce Research (Wiley).

GALIT SHMUELI, PhD, is Associate Professor of Statistics and Director of the eMarkets Research Lab in the Robert H. Smith School of Business at the University of Maryland. Her research focuses on statistical strategy and data mining methods for scientific research and real-world applications. Dr. Shmueli has published over sixty journal articles on statistical and data mining methods related to online auctions and biosurveillance. She is the coauthor of Statistical Methods in e-Commerce Research and Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel® with XLMiner®, Second Edition, both published by Wiley.

Read More Show Less

Table of Contents

Preface.

Acknowledgments.

1 Introduction.

1.1 Online Auctions and Electronic Commerce.

1.2 Online Auctions and Statistical Challenges.

1.3 A Statistical Approach to Online Auction Research.

1.4 The Structure of this Book.

1.5 Data and Code Availability.

2 Obtaining Online Auction Data.

2.1 Collecting Data from the Web.

2.2 Web Data Collection and Statistical Sampling.

3 Exploring Online Auction Data.

3.1 Bid Histories: Bids versus "Current Price" Values.

3.2 Integrating Bid History Data With Cross-Sectional Auction Information.

3.3 Visualizing Concurrent Auctions.

3.4 Exploring Price Evolution and Price Dynamics.

3.5 Combining Price Curves with Auction Information via Interactive Visualization.

3.6 Exploring Hierarchical Information.

3.7 Exploring Price Dynamics via Curve Clustering.

3.8 Exploring Distributional Assumptions.

3.9 Exploring Online Auctions: Future Research Directions.

4 Modeling Online Auction Data.

4.1 Modeling Basics (Representing the Price Process).

4.2 Modeling The Relation Between Price Dynamics and Auction Information.

4.3 Modeling Auction Competition.

4.4 Modeling Bid and Bidder Arrivals.

4.5 Modeling Auction Networks.

5 Forecasting Online Auctions.

5.1 Forecasting Individual Auctions.

5.2 Forecasting Competing Auctions.

5.3 Automated Bidding Decisions.

Bibliography.

Index.

Read More Show Less

Customer Reviews

Average Rating 4
( 1 )
Rating Distribution

5 Star

(0)

4 Star

(1)

3 Star

(0)

2 Star

(0)

1 Star

(0)

Your Rating:

Review Guidelines
Add Recommendations
Your Name: Create a Pen Name or

Barnes & Noble.com Review Rules

Our reader reviews allow you to share your comments on titles you liked, or didn't, with others. By submitting an online review, you are representing to Barnes & Noble.com that all information contained in your review is original and accurate in all respects, and that the submission of such content by you and the posting of such content by Barnes & Noble.com does not and will not violate the rights of any third party. Please follow the rules below to help ensure that your review can be posted.

Reviews by Our Customers Under the Age of 13

We highly value and respect everyone's opinion concerning the titles we offer. However, we cannot allow persons under the age of 13 to have accounts at BN.com or to post customer reviews. Please see our Terms of Use for more details.

What to exclude from your review:

Please do not write about reviews, commentary, or information posted on the product page. If you see any errors in the information on the product page, please send us an email.

Reviews should not contain any of the following:

  • - HTML tags, profanity, obscenities, vulgarities, or comments that defame anyone
  • - Time-sensitive information such as tour dates, signings, lectures, etc.
  • - Single-word reviews. Other people will read your review to discover why you liked or didn't like the title. Be descriptive.
  • - Comments focusing on the author or that may ruin the ending for others
  • - Phone numbers, addresses, URLs
  • - Pricing and availability information or alternative ordering information
  • - Advertisements or commercial solicitation

Reminder:

  • - By submitting a review, you grant to Barnes & Noble.com and its sublicensees the royalty-free, perpetual, irrevocable right and license to use the review in accordance with the Barnes & Noble.com Terms of Use.
  • - Barnes & Noble.com reserves the right not to post any review -- particularly those that do not follow the terms and conditions of these Rules. Barnes & Noble.com also reserves the right to remove any review at any time without notice.
  • - See Terms of Use for other conditions and disclaimers.
Search for Products You'd Like to Recommend

Recommend other products that relate to your review. Just search for them below and share!

Create a Pen Name

Your Pen Name is your unique identity on BN.com. It will appear on the reviews you write and other website activities. Your Pen Name cannot be edited, changed or deleted once submitted.

 
Your Pen Name can be any combination of alphanumeric characters (plus - and _), and must be at least two characters long.

Continue Anonymously
Sort by: Showing 1 Customer Review

  • Posted January 12, 2011

    more from this reviewer

    focuses on understanding eBay

    This book can be read as a sequel to an earlier text, Statistical Methods in e-Commerce Research (Statistics in Practice). That looked at various types of websites, like Amazon and Wikipedia, while the current offering specialises to the key case of studying auction websites. Of these, eBay dominates the discussion, simply because it is the largest such website on the Internet. In a way, the exposition is simpler than it might have been just a few years ago, when Amazon and Yahoo also ran auctions. They were keying off eBay's success and hoped to take some of that business. But their efforts came to nought, so that now if you study online auctions, it is really only eBay and a handful of much smaller entities like uBid.

    The screen captures in chapter 2 of typical web pages from an auction [on eBay] shows the complex spaghetti-like source code. Note that if you do decide to screen scrape, then this is brittle, since if the website makes just minor changes in the format of their pages, extensive changes to your parsing might be necessary, to extract the same information. But as the authors make clear, screen scraping has the advantage of being free. An alternative is to use a Web Service, if that is offered by the website. Much more efficient and robust. But not all websites have this, and those that do could require payment. Plus the information offered by their Web Service might not include data that you need.

    Chapter 3 tackles the problem of how to simulate data using continuous distributions, when actual auction data often looks like a mixture of continuous and spiky inputs, where the latter are bids of high frequency, that stand significantly above the rest of the bid distribution.

    Chapter 4 discusses various models - exponential, log, logistic and inverse logistic, that can be used to model a given auction. But the problem is that across a set of auctions, even for instances of the same item being offered, all such models might be observed. Where a specific auction could be best fitted by a log, say, while another auction looks like a logistic. The authors suggest a "Beta()" function that has only 2 parameters. This turns out to be easy to compute, and, depending on the choices of parameter values, can replicate each of the 4 earlier models. Just as importantly, the fitting of a Beta to a given auction can be automated, which gets around an earlier problem of having to make a manual choice between one of the earlier models.

    Perhaps as interestingly, the chapter goes further, into studying what the book calls the spatial similarity between auctions. The spatial refers to auctions where items differ slightly. For example, a gaming computer that has different colours, and different disk sizes and different memory sizes installed. This is a multidimensional feature space, where the features might differ continuously [like memory] or discretely [like colour]. It reflects the well known attraction of eBay where if you search for a popular item, you can find hundreds [or even thousands] that differ in parameters like these. Modelling the behaviour of bidders when confronted by a surfeit of choices would be good. The authors show a way to tackle how to define a metric in the feature space.

    Was this review helpful? Yes  NoThank you for your feedback.   Report this reviewThank you, this review has been flagged.
Sort by: Showing 1 Customer Review
If you find inappropriate content, please report it to Barnes & Noble
Why is this product inappropriate?
Comments (optional)