Modeling online auctions
Author(s)
Bibliographic Information
Modeling online auctions
（Statistics in practice）
Wiley, c2010
- : harback
Available at / 7 libraries
-
No Libraries matched.
- Remove all filters.
Note
Includes bibliographical references (p. 301-312) and index
Description and Table of Contents
Description
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
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.
by "Nielsen BookData"
