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 href=’http://modelingonlineauctions.com/’>here
Cuprins
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.
Despre autor
WOLFGANG JANK, Ph D, 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, Ph D, is Associate Professor of Statistics
and Director of the e Markets 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.
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