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E-raamat: Modeling Online Auctions

(University of Maryland), (University of Maryland)
  • Formaat: PDF+DRM
  • Sari: Statistics in Practice
  • Ilmumisaeg: 05-May-2010
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9780470642597
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  • Formaat: PDF+DRM
  • Sari: Statistics in Practice
  • Ilmumisaeg: 05-May-2010
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9780470642597
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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 books 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. Send Comment

Arvustused

"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)

Preface ix
Acknowledgments xi
1 Introduction
1(8)
1.1 Online Auctions and Electronic Commerce
3(1)
1.2 Online Auctions and Statistical Challenges
4(2)
1.3 A Statistical Approach to Online Auction Research
6(1)
1.4 The Structure of this Book
6(2)
1.5 Data and Code Availability
8(1)
2 Obtaining Online Auction Data
9(22)
2.1 Collecting Data from the Web
9(9)
2.2 Web Data Collection and Statistical Sampling
18(13)
3 Exploring Online Auction Data
31(65)
3.1 Bid Histories: Bids versus "Current Price" Values
32(4)
3.2 Integrating Bid History Data With Cross-Sectional Auction Information
36(5)
3.3 Visualizing Concurrent Auctions
41(3)
3.4 Exploring Price Evolution and Price Dynamics
44(13)
3.5 Combining Price Curves with Auction Information via Interactive Visualization
57(3)
3.6 Exploring Hierarchical Information
60(3)
3.7 Exploring Price Dynamics via Curve Clustering
63(9)
3.8 Exploring Distributional Assumptions
72(22)
3.9 Exploring Online Auctions: Future Research Directions
94(2)
4 Modeling Online Auction Data
96(157)
4.1 Modeling Basics (Representing the Price Process)
97(35)
4.2 Modeling The Relation Between Price Dynamics and Auction Information
132(25)
4.3 Modeling Auction Competition
157(32)
4.4 Modeling Bid and Bidder Arrivals
189(49)
4.5 Modeling Auction Networks
238(15)
5 Forecasting Online Auctions
253(48)
5.1 Forecasting Individual Auctions
254(25)
5.2 Forecasting Competing Auctions
279(12)
5.3 Automated Bidding Decisions
291(10)
Bibliography 301(12)
Index 313
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.