网上拍卖竞买者出价水平的动态演变模式研究

网上拍卖是将传统拍卖与网络相结合的一种新商务模式,是对资源进行有效配置的一种市场机制。尽管有关参与者行为的研究是网上拍卖中的一个重要论题,但是国际上鲜有文献从统计视角对竞买者出价水平的动态演变模式及其变异性进行研究。本文利用函数性数据分析方法及基于MatLab编写的计算程序,对从eBay网站收集到的27个拍卖子类的出价数据进行了分析,结果显示竞买者出价水平的动态演变呈现“c”形模式且其形状随 拍卖的不同特征而变化,“c”形模式变异的两个主要方式是“前期变化”和“后期变化”,其显著变异的时段是拍卖的早期阶段。本文为网上拍卖问题的实证研究引入了新方法并提供了技术支持,所得结论丰富和发展了现有相关研究文献的结论。     

大数据视域下的企业统计创新——生产型企业统计问题与对策研究

大数据时代，传统生产型企业如何加强企业自身与周边相关数据信息的统计与分析是一个亟待关注的问题。本文对生产型企业发生的数据现状与特点进行了总结与概括，深入分析了企业统计工作中存在的精细化程度不够、数据实时性不够等问题，并结合生产型企业的特点，从五个方面提出改进措施，希望对企业提升统计分析能力，以数据支撑科学决策提供一定的借鉴价值。     

竞买者出价来到过程的数学模型及其应用研究

利用基于MatLab编写的计算程序，对从eBay网站收集的20个拍卖子类的竞买者出价数据进行了分析，发现网上拍卖中竞买者出价次数随拍卖进程呈现三阶段特征；竞买者出价速率具有时变性，可利用Shmueli等给出的三段速率函数表达式进行描述；三段出价速率函数的非齐次泊松过程很好地刻画了网上拍卖中竞买者出价来到过程的特征。     

上证、深证及道琼斯指数收益率的统计特征

通过对道琼斯工业平均指数,上证指数和深证成指收益率分布的统计和分析,说明了它们具有“高峰厚尾”、“有偏”等统计现象。经过对数据的对比和处理,以求解释这些统计现象的产生原因。通过对三个指数收益率绝对值的统计分析,得出了收益率绝对值的分布特征。     

浅谈统计分析方法与写作

统计分析是统计活动的重要内容，是近年来受到统计部门及有关部门和单位特别重视的一项工作、统计工作一般分为统计设计、统计调查、统计整理、统计分析四个阶段。统计分析阶段是利用科学的方法，对收集、整理出来的统计资料进行精密加工、分析研究，对所调查的社会经济现象的特点、本质、规律逐步进行深入的认识，得出相应的结论的阶段。从内容上看，统计分析包括统计分析活动、统计分析方法、统计分析成果三个方面。统计分析活动是对客观现象的一种认识活动，其数量性和总体性是区别于其他认识活动的基本特点。统计分析活动的数量性是指统…     

深市、沪市地产股指数收益率波动性的统计研究

本文研究了深市、沪市地产指数波动的统计性质。我们主要对深市、沪市地产指数2001年-2006年的日收益率进行研究。首先从平稳序列的角度,采用偏度峰度检验和 Kolmogorov-Smirnov 检验等方法对两证券市场的地产指数收益率分布进行了实证分析,研究结果表明中国证券市场综合地产指数收益率序列与Gauss分布具有一定的偏离。进一步地根据数据统计分析,得到两证券市场的地产指数收益率服从幂率分布。论文的最后对地产指数的相关价格进行统计分析,讨论其相应的统计规律性。     

探索性数据分析中的统计图形应用

在对复杂调查数据进行探索性统计分析时,沿用常规的一些统计图形可能使得图形无法解释或导致误导性的视觉效果.在归纳复杂调查数据特点的基础上,讨论了针对这些特点,如何基于改进的一些统计图形对复杂调查数据进行探索性分析.     

大学生月消费情况调查——以南京邮电大学为例

本文通过网络问卷调查的方式，在南京邮电大学内网上进行大学生月消费情况调查，并分别采用描述统计、假设检验（t检验）、列联表分析（拟合优度检验）和回归分析等统计方法对数据进行统计分析，得出：大学生月平均消费为1002元，男生和女生月消费均值相等，男生和女生中非理性消费的比例一致，大学生月支出占家庭月收入的近似比值为0．289。     

Excel在统计工作中的应用

一项完整的统计工作，通常包括搜集资料、统计整理、绘制图形、处理数据，最终写出高质量的统计分析报告。我们在实际工作中常常用计算机来代替手工操作，但由于某些制图软件、数据库软件以及一些统计分析软件所具备的功能都是单方面的，对于一项完整的统计工作来说，分别使用这些软件，既不统一又不联贯。这里我们介绍一种“表格”式的数据综合管理与分析系统，即Ｅｘｃｅｌ，它集文字、数据、图形、图表以及其他多媒体对象于一体，以电子表格形式来进行各种计算、分析和管理工作。文章试从统计表的建立、统计制图、数据处理及统计分析这四个方面加以举例说明     

浅谈现代统计分析

现代统计分析是开发统计信息，进行统计咨询、实施统计监督的重要手段。是在当前改革开放形势下，对经济实行宏观调控的科学决策的重要依据。本文针对信息革命热潮中对统计信息全方位、深层次开发的需要，提出了现代统计分析与传统统计分析的不同之点，讨论了计算机软、硬件的发展对现代统计分析方法的影响，介绍了一种实现现代统计分析的有力工具──ＳＰＳＳ软件新版本的主要特点。     

Explaining and Forecasting Online Auction Prices and Their Dynamics Using Functional Data Analysis

Online auctions have become increasingly popular in recent years, and as a consequence there is a growing body of empirical research on this topic. Most of that research treats data from online auctions as cross-sectional, and consequently ignores the changing dynamics that occur during an auction. In this article we take a different look at online auctions and propose to study an auction's price evolution and associated price dynamics. Specifically, we develop a dynamic forecasting system to predict the price of an ongoing auction. By dynamic, we mean that the model can predict the price of an auction “in progress” and can update its prediction based on newly arriving information. Forecasting price in online auctions is challenging because traditional forecasting methods cannot adequately account for two features of online auction data: (1) the unequal spacing of bids and (2) the changing dynamics of price and bidding throughout the auction. Our dynamic forecasting model accounts for these special features by using modern functional data analysis techniques. Specifically, we estimate an auction's price velocity and acceleration and use these dynamics, together with other auction-related information, to develop a dynamic functional forecasting model. We also use the functional context to systematically describe the empirical regularities of auction dynamics. We apply our method to a novel set of Harry Potter and Microsoft Xbox data and show that our forecasting model outperforms traditional methods.     

Modelling concurrency of events in on-line auctions via spatiotemporal semiparametric models

Summary.  We introduce a semiparametric approach for modelling the effect of concurrent events on an outcome of interest. Concurrency manifests itself as temporal and spatial dependences. By temporal dependence we mean the effect of an event in the past. Modelling this effect is challenging since events arrive at irregularly spaced time intervals. For the spatial part we use an abstract notion of 'feature space' to conceptualize distances among a set of item features. We motivate our model in the context of on-line auctions by modelling the effect of concurrent auctions on an auction's price. Our concurrency model consists of three components: a transaction-related component that accounts for auction design and bidding competition, a spatial component that takes into account similarity between item features and a temporal component that accounts for recently closed auctions. To construct each of these we borrow ideas from spatial and mixed model methodology. The power of this model is illustrated on a large and diverse set of laptop auctions on eBay.com. We show that our model results in superior predictive performance compared with a set of competitor models. The model also allows for new insight into the factors that drive price in on-line auctions and their relationship to bidding competition, auction design, product variety and temporal learning effects.     

基于函数性数据方法的艺术品网上拍卖竞买者出价水平影响因素研究

在分析互联网艺术品拍卖中竞买者出价水平特有影响因素的基础上,运用函数性相平面图与线性回归方法,探究出价水平及各影响因素在整个拍卖期间的动态变化情况。结果表明,在拍卖的不同阶段,推动竞买者出价水平变化的影响因素各有不同,且作用大小与作用方向在整个拍卖期间不断变化。特别指出,拍卖次序在整个拍卖期间对出价水平具有正向影响,且作用程度随着拍卖的进行逐渐增强,即序号越大、越晚参与竞拍的艺术品的最终成交金额越大。     

Random-Time Aggregation in Partial Adjustment Models

Structural econometric auction models with explicit game-theoretic modeling of bidding strategies have been quite a challenge from a methodological perspective, especially within the common value framework. We develop a Bayesian analysis of the hierarchical Gaussian common value model with stochastic entry introduced by Bajari and Hortaçsu. A key component of our approach is an accurate and easily interpretable analytical approximation of the equilibrium bid function, resulting in a fast and numerically stable evaluation of the likelihood function. We extend the analysis to situations with positive valuations using a hierarchical gamma model. We use a Bayesian variable selection algorithm that simultaneously samples the posterior distribution of the model parameters and does inference on the choice of covariates. The methodology is applied to simulated data and to a newly collected dataset from eBay with bids and covariates from 1000 coin auctions. We demonstrate that the Bayesian algorithm is very efficient and that the approximation error in the bid function has virtually no effect on the model inference. Both models fit the data well, but the Gaussian model outperforms the gamma model in an out-of-sample forecasting evaluation of auction prices. This article has supplementary material online.     

On the Estimation of the Density of a Directional Data Stream

Many directional data such as wind directions can be collected extremely easily so that experiments typically yield a huge number of data points that are sequentially collected. To deal with such big data, the traditional nonparametric techniques rapidly require a lot of time to be computed and therefore become useless in practice if real time or online forecasts are expected. In this paper, we propose a recursive kernel density estimator for directional data which (i) can be updated extremely easily when a new set of observations is available and (ii) keeps asymptotically the nice features of the traditional kernel density estimator. Our methodology is based on Robbins–Monro stochastic approximations ideas. We show that our estimator outperforms the traditional techniques in terms of computational time while being extremely competitive in terms of efficiency with respect to its competitors in the sequential context considered here. We obtain expressions for its asymptotic bias and variance together with an almost sure convergence rate and an asymptotic normality result. Our technique is illustrated on a wind dataset collected in Spain. A Monte‐Carlo study confirms the nice properties of our recursive estimator with respect to its non‐recursive counterpart.     

Sufficiency Revisited: Rethinking Statistical Algorithms in the Big Data Era

The big data era demands new statistical analysis paradigms, since traditional methods often break down when datasets are too large to fit on a single desktop computer. Divide and Recombine (D&R) is becoming a popular approach for big data analysis, where results are combined over subanalyses performed in separate data subsets. In this article, we consider situations where unit record data cannot be made available by data custodians due to privacy concerns, and explore the concept of statistical sufficiency and summary statistics for model fitting. The resulting approach represents a type of D&R strategy, which we refer to as summary statistics D&R; as opposed to the standard approach, which we refer to as horizontal D&R. We demonstrate the concept via an extended Gamma–Poisson model, where summary statistics are extracted from different databases and incorporated directly into the fitting algorithm without having to combine unit record data. By exploiting the natural hierarchy of data, our approach has major benefits in terms of privacy protection. Incorporating the proposed modelling framework into data extraction tools such as TableBuilder by the Australian Bureau of Statistics allows for potential analysis at a finer geographical level, which we illustrate with a multilevel analysis of the Australian unemployment data. Supplementary materials for this article are available online.     

Integrated-Quantile-Based Estimation for First-Price Auction Models

This article considers nonparametric estimation of first-price auction models under the monotonicity restriction on the bidding strategy. Based on an integrated-quantile representation of the first-order condition, we propose a tuning-parameter-free estimator for the valuation quantile function. We establish its cube-root-n consistency and asymptotic distribution under weaker smoothness assumptions than those typically assumed in the empirical literature. If the latter are true, we also provide a trimming-free smoothed estimator and show that it is asymptotically normal and achieves the optimal rate of Guerre, Perrigne, and Vuong (2000). We illustrate our method using Monte Carlo simulations and an empirical study of the California highway procurement auctions. Supplementary materials for this article are available online.     

A multilevel model with autoregressive components for the analysis of tribal art prices

In this paper, we introduce a multilevel model specification with time-series components for the analysis of prices of artworks sold at auctions. Since auction data do not constitute a panel or a time series but are composed of repeated cross-sections, they require a specification with items at the first level nested in time-points. Our approach combines the flexibility of mixed effect models together with the predicting performance of time series as it allows to model the time dynamics directly. Model estimation is obtained by means of maximum likelihood through the expectation–maximization algorithm. The model is motivated by the analysis of the first database ethnic artworks sold in the most important auctions worldwide. The results show that the proposed specification improves considerably over classical proposals both in terms of fit and prediction.     

空间数据分析的发展

空间统计学是研究空间问题的一门学科,它是应用数学快速发展的一个分支。尽管传统的数据分析中有许多很好的方法,但却不能完全地套用于空间数据的分析。空间模型的估计不仅与各种回归形式的假设有关,而且还与空间相关、空间异质的特性有关。从空间模型及推断、适应性估计、非参数回归、空间不相关性检验几个方面研究了空间数据分析方法的发展以及未来的趋势。