Forecasting the price in online auctions is important for buyers and sellers. With good forecasts, bidders can make informed bidding decisions and sellers can select the right time and place to list their products. While information from other auctions can help forecast an ongoing auction, it...

Applying statistical tools to help understand business processes and make informed business decisions has attracted enormous amount of research interests in recent years. In this dissertation, we develop and apply data mining techniques to two sources of data, online bidding data for eBay and...

The flexibility of time and location as well as the availability of an abundance of both old and new products makes online auctions an important part of people's daily shopping experience. While many bidders rely on variants of the well-documented early or last-minutes bidding strategies,...

Functional data analysis can be challenging when the functional objects are sampled only very sparsely and unevenly. Most approaches rely on smoothing to recover the underlying functional object from the data which can be difficult if the data is irregularly distributed. In this paper we present...

The wide and growing popularity of online auctions creates enormous amounts of publicly available bid data providing an important topic for research. These data pose unique statistical challenges because of their special structures. This research focuses on methods for representing, visualizing,...

Despite the growing research interest in Internet auctions, particularly those on eBay, little is known about quantifiable consumer surplus levels in such mechanisms. Using an ongoing novel field experiment that involves real bidders participating in real auctions, and voting with real dollars,...

Empirical research of online auctions has dramatically grown in recent years. Studies using publicly available bid data from websites such as eBay.com have found many divergences of bidding behavior and auction outcomes compared to ordinary offline auctions and auction theory. Among the main...

Semi-continuous data arise in many applications where naturally-continuous data become contaminated by the data generating mechanism. The resulting data contain several values that are too frequent, and in that sense are a hybrid between discrete and continuous data. The main problem is that...

Summary.We introduce a new family of non-homogeneous Poisson processes (NHPP) that are useful for modeling pure and contaminated self-similar processes which describe arrivals within a finite time period. Our motivation comes from the bid arrival process in online auctions.Modeling bid arrivals...