电子商务环境下消费者个人信息保护危机与应对——以新制度经济学为视角

电子商务环境中,经营者常利用其与消费者之间的非均衡博弈关系擅自处理后者的个人信息,这不但侵害了消费者的人格利益,而且阻碍了个人信息资源的高效配置。我国为促使经营者尊重消费者对个人信息的排他性产权,应强制经营者在处理个人信息前履行征得消费者许可等义务,进而改变这种不均衡状态。而立法者为了优化规制效果,宜立足于商业伦理与网络治理等因素构筑的特殊国情并重点借鉴欧盟的成例,凭借作为公共选择的立法活动将前述义务予以明确化。但刚性的法律规范又会不当限制经营者与消费者的决策自由,并阻碍个人信息的有效利用,对此,我国还需要参照美欧做法对经营者义务规则的适用设定例外情形。     

整体波场延拓法基准面校正

提出了一种新的近地表问题解决方法——整体波场延拓法基准面校正(WEDD)。借助地震理论中的互换原理和观测面沉降概念,在基准面校正过程中将炮点和检波点的处理统一起来,并利用炮点-全偏移距域双平方根算子实现波场的单域整体延拓,最后提取目标深度的波场值完成参考面的重新定义。模型试算结果表明了WEDD方法的有效性。     

整体波场延拓法基准面校正与常规方法对比

消除近地表对地震波场所造成的影响,常规的解决方法是静校正和波动方程基准面校正。当勘探区域近地表出现较大的地形起伏或速度横向变化时,波动方程基准面校正则成为唯一有效的选择。传统波动方程基准面校正在实现过程中需要在双域对炮点、检波点分别进行延拓成像。为此,作者曾提出一种新的近地表问题解决方法——整体波场延拓法基准面校正(WEDD)。本文对WEDD方法和常规基准面校正方法在理论上进行了对比分析,并通过数值模拟讨论了各基准面校正方法的优缺点和适用范围。     

论网约车平台经营者集中现象的反垄断规制——以中俄Uber并购案为分析视角

作为互联网新兴领域,网约车平台经营者集中现象为反垄断工作带来巨大挑战。对于2016年8月滴滴Uber合并案中合并后的滴滴是否构成垄断并采取反垄断措施,社会与学术界争论颇多。文章通过域外判例借鉴及法律分析,结合互联网平台行业发展的特有规律,得出本案的竞争市场应界定为网约车市场、滴滴平台占据市场支配地位,尚未产生排除、限制竞争效果,因此目前无需采取反垄断制裁的结论;同时针对我国网约车平台市场竞争态势,对未来网约车平台垄断行为规制提出完善建议:应当对我国网约车平台采取包容审慎的反垄断规制态度,既要考虑网络特点,又要实现对国内企业的有效保护;并从发挥大数据优势、合理降低准入门槛、建立创新鼓励模式三个角度入手提出具体的规制建议,以期为我国互联网平台经营者集中这一现象的反垄断分析提供新的思路。     

A simple approach for varying-coefficient model selection

In varying-coefficient models, an important question is to determine whether some of the varying coefficients are actually invariant coefficients. This article proposes a penalized likelihood method in the framework of the smoothing spline ANOVA models, with a penalty designed toward the goal of automatically distinguishing varying coefficients and those which are not varying. Unlike the stepwise procedure, the method simultaneously quantifies and estimates the coefficients. An efficient algorithm is given and ways of choosing the smoothing parameters are discussed. Simulation results and an analysis on the Boston housing data illustrate the usefulness of the method. The proposed approach is further extended to longitudinal data analysis.     

A First-Order Spatial Integer-Valued Autoregressive SINAR(1, 1) Model

Binomial thinning operator has a major role in modeling one-dimensional integer-valued autoregressive time series models. The purpose of this article is to extend the use of such operator to define a new stationary first-order spatial non negative, integer-valued autoregressive SINAR(1, 1) model. We study some properties of this model like the mean, variance and autocorrelation function. Yule-Walker estimator of the model parameters is also obtained. Some numerical results of the model are presented and, moreover, this model is applied to a real data set.     

一类指数型幂级数的求和

应用差分算子给出了一类指数型幂级数的一个求和公式，并给出了其在组合恒等式方面的一些应用     

A new class of INAR(1) model for count time series

The present work proposes a new integer valued autoregressive model with Poisson marginal distribution based on the mixing Pegram and dependent Bernoulli thinning operators. Properties of the model are discussed. We consider several methods for estimating the unknown parameters of the model. Also, the classical and Bayesian approaches are used for forecasting. Simulations are performed for the performance of these estimators and forecasting methods. Finally, the analysis of two real data has been presented for illustrative purposes.     

The Complete Gradient Clustering Algorithm: properties in practical applications

The aim of this paper is to present a Complete Gradient Clustering Algorithm, its applicational aspects and properties, as well as to illustrate them with specific practical problems from the subject of bioinformatics (the categorization of grains for seed production), management (the design of a marketing support strategy for a mobile phone operator) and engineering (the synthesis of a fuzzy controller). The main property of the Complete Gradient Clustering Algorithm is that it does not require strict assumptions regarding the desired number of clusters, which allows to better suit its obtained number to a real data structure. In the basic version it is possible to provide a complete set of procedures for defining the values of all functions and parameters relying on the optimization criterions. It is also possible to point out parameters, the potential change which implies influence on the size of the number of clusters (while still not giving an exact number) and the proportion between their numbers in dense and sparse areas of data elements. Moreover, the Complete Gradient Clustering Algorithm can be used to identify and possibly eliminate atypical elements (outliers). These properties proved to be very useful in the presented applications and may also be functional in many other practical problems.