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Representing, visualizing, and modeling online auction data .

机译：表示，可视化和建模在线拍卖数据。

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摘要

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, and modeling such data.; We find semi-continuous data manifested in auction consumer surplus data. 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", a hybrid between discrete and continuous data. The main problem is that standard statistical methods, which are geared towards continuous or discrete data, cannot be applied adequately to semi-continuous data. We propose a new set of two transformations for semi-continuous data that "iron-out" the too-frequent values into completely continuous data. We show that the transformed data maintain the properties of the original data but are suitable for standard analysis.; We are also interested in the effect of concurrency not only on the final price of an auction but also on the relationship between the current bid levels and high bids in simultaneous ongoing auctions. We suggest several ways to visually represent the concurrent nature of online auction prices. These include "rug plots" for displaying the price-evolution and price dynamics in concurrent auctions, time-grouped box plots, and moving statistics plots. We find price trends and relationships between prices in concurrent auctions and raise new research questions.; Finally, bids during an online auction arrive at unequally-spaced discrete time points. Our goal is to capture the entire continuous price-evolution function by representing it as a functional object. Various nonparametric smoothing methods exist to estimate the functional object from the observed discrete bid data. Previous studies use penalized polynomial and monotone smoothing splines; however, these require the determination of a large number of coefficients and often lengthy computational time. We present a family of parametric growth curves that describe the price-evolution during online auctions. Our approach is parsimonious and has an appealing interpretation in the online auction context. We also provide an automated fitting algorithm that is computationally fast.
机译：在线拍卖的广泛且不断增长的流行创造了大量公开可用的出价数据，为研究提供了重要的话题。这些数据由于其特殊的结构而带来了独特的统计挑战。这项研究的重点是表示，可视化和建模此类数据的方法。我们发现拍卖消费者剩余数据中显示出半连续数据。半连续数据出现在许多应用中，其中自然连续数据被数据生成机制污染。生成的数据包含“太频繁”的几个值，离散和连续数据之间是混合的。主要问题是，针对连续或离散数据的标准统计方法无法充分应用于半连续数据。我们为半连续数据提出了一套新的两次转换，可以将太频繁的值“熨平”为完全连续的数据。我们表明，转换后的数据保留了原始数据的属性，但适用于标准分析。我们还对并发性不仅对拍卖的最终价格感兴趣，而且对同时进行的拍卖中当前出价水平和高出价之间的关系感兴趣。我们建议以几种方式形象地表示在线拍卖价格的并发性质。这些包括用于显示并发拍卖中价格演变和价格动态的“地毯图”，按时间分组的箱形图和移动统计图。我们在并行拍卖中发现价格趋势以及价格之间的关系，并提出了新的研究问题。最终，在线拍卖期间的出价到达间隔不等的离散时间点。我们的目标是通过将其表示为功能对象来捕获整个连续的价格演化函数。存在各种非参数平滑方法，用于从观察到的离散投标数据中估计功能对象。先前的研究使用罚分多项式和单调平滑样条。但是，这些要求确定大量系数，并且通常需要很长的计算时间。我们提供了一系列参数增长曲线，这些曲线描述了在线拍卖期间的价格演变。我们的方法是简约的，并且在在线拍卖中具有有吸引力的解释。我们还提供了计算速度快的自动拟合算法。

著录项

  • 作者

    Hyde, Valerie.;

  • 作者单位

    University of Maryland, College Park.$bApplied Mathematics and Scientific Computation.;

  • 授予单位 University of Maryland, College Park.$bApplied Mathematics and Scientific Computation.;
  • 学科 Statistics.; Economics Commerce-Business.
  • 学位 Ph.D.
  • 年度 2007
  • 页码 161 p.
  • 总页数 161
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 统计学;贸易经济;
  • 关键词

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