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.

"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 xi1 Introduction 11.1 Online Auctions and Electronic Commerce, 31.2 Online Auctions and Statistical Challenges, 41.3 A Statistical Approach to Online Auction Research, 61.4 The Structure of this Book, 61.5 Data and Code Availability, 82 Obtaining Online Auction Data 92.1 Collecting Data from the Web, 92.2 Web Data Collection and Statistical Sampling, 183 Exploring Online Auction Data 313.1 Bid Histories: Bids versus “Current Price” Values, 323.2 Integrating Bid History Data With Cross-Sectional Auction Information, 363.3 Visualizing Concurrent Auctions, 413.4 Exploring Price Evolution and Price Dynamics, 443.5 Combining Price Curves with Auction Information via Interactive Visualization, 573.6 Exploring Hierarchical Information, 603.7 Exploring Price Dynamics via Curve Clustering, 633.8 Exploring Distributional Assumptions, 723.9 Exploring Online Auctions: Future Research Directions, 944 Modeling Online Auction Data 964.1 Modeling Basics (Representing the Price Process), 974.2 Modeling The Relation Between Price Dynamics and Auction Information, 1324.3 Modeling Auction Competition, 1574.4 Modeling Bid and Bidder Arrivals, 1894.5 Modeling Auction Networks, 2385 Forecasting Online Auctions 2535.1 Forecasting Individual Auctions, 2545.2 Forecasting Competing Auctions, 2795.3 Automated Bidding Decisions, 291Bibliography 301Index 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.

"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 xi1 Introduction 11.1 Online Auctions and Electronic Commerce, 31.2 Online Auctions and Statistical Challenges, 41.3 A Statistical Approach to Online Auction Research, 61.4 The Structure of this Book, 61.5 Data and Code Availability, 82 Obtaining Online Auction Data 92.1 Collecting Data from the Web, 92.2 Web Data Collection and Statistical Sampling, 183 Exploring Online Auction Data 313.1 Bid Histories: Bids versus “Current Price” Values, 323.2 Integrating Bid History Data With Cross-Sectional Auction Information, 363.3 Visualizing Concurrent Auctions, 413.4 Exploring Price Evolution and Price Dynamics, 443.5 Combining Price Curves with Auction Information via Interactive Visualization, 573.6 Exploring Hierarchical Information, 603.7 Exploring Price Dynamics via Curve Clustering, 633.8 Exploring Distributional Assumptions, 723.9 Exploring Online Auctions: Future Research Directions, 944 Modeling Online Auction Data 964.1 Modeling Basics (Representing the Price Process), 974.2 Modeling The Relation Between Price Dynamics and Auction Information, 1324.3 Modeling Auction Competition, 1574.4 Modeling Bid and Bidder Arrivals, 1894.5 Modeling Auction Networks, 2385 Forecasting Online Auctions 2535.1 Forecasting Individual Auctions, 2545.2 Forecasting Competing Auctions, 2795.3 Automated Bidding Decisions, 291Bibliography 301Index 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.

"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 xi1 Introduction 11.1 Online Auctions and Electronic Commerce, 31.2 Online Auctions and Statistical Challenges, 41.3 A Statistical Approach to Online Auction Research, 61.4 The Structure of this Book, 61.5 Data and Code Availability, 82 Obtaining Online Auction Data 92.1 Collecting Data from the Web, 92.2 Web Data Collection and Statistical Sampling, 183 Exploring Online Auction Data 313.1 Bid Histories: Bids versus “Current Price” Values, 323.2 Integrating Bid History Data With Cross-Sectional Auction Information, 363.3 Visualizing Concurrent Auctions, 413.4 Exploring Price Evolution and Price Dynamics, 443.5 Combining Price Curves with Auction Information via Interactive Visualization, 573.6 Exploring Hierarchical Information, 603.7 Exploring Price Dynamics via Curve Clustering, 633.8 Exploring Distributional Assumptions, 723.9 Exploring Online Auctions: Future Research Directions, 944 Modeling Online Auction Data 964.1 Modeling Basics (Representing the Price Process), 974.2 Modeling The Relation Between Price Dynamics and Auction Information, 1324.3 Modeling Auction Competition, 1574.4 Modeling Bid and Bidder Arrivals, 1894.5 Modeling Auction Networks, 2385 Forecasting Online Auctions 2535.1 Forecasting Individual Auctions, 2545.2 Forecasting Competing Auctions, 2795.3 Automated Bidding Decisions, 291Bibliography 301Index 313