电子商务法律保护初探

电子商务的迅速发展对与之相应的法律保护手段提出了新的要求。首先是电子商务的标准应在世界范围内有一个最低限度的确定。其次是建立科学的身份认证制度,让适格的主体进入网络市场。再次是应承认电子证据的法律效力和全球范围内谋求纠纷管辖权的协调。     

论铁路交通事故归责原则

目前我国有关交通事故的立法严重滞后,理论界和实务部门对铁路交通事故归责原则众说纷纭。鉴于我国的国情,铁路交通事故应适用过错责任原则。     

Asymptotic properties of maximum quasi-likelihood estimator in quasi-likelihood non linear models with stochastic regression

In this paper, we establish the asymptotic properties of maximum quasi-likelihood estimator (MQLE) in quasi-likelihood non linear models (QLNMs) with stochastic regression under some mild regular conditions. We also investigate the existence, strong consistency, and asymptotic normality of MQLE in QLNMs with stochastic regression.     

Principal Volatility Component Analysis

Many empirical time series such as asset returns and traffic data exhibit the characteristic of time-varying conditional covariances, known as volatility or conditional heteroscedasticity. Modeling multivariate volatility, however, encounters several difficulties, including the curse of dimensionality. Dimension reduction can be useful and is often necessary. The goal of this article is to extend the idea of principal component analysis to principal volatility component (PVC) analysis. We define a cumulative generalized kurtosis matrix to summarize the volatility dependence of multivariate time series. Spectral analysis of this generalized kurtosis matrix is used to define PVCs. We consider a sample estimate of the generalized kurtosis matrix and propose test statistics for detecting linear combinations that do not have conditional heteroscedasticity. For application, we applied the proposed analysis to weekly log returns of seven exchange rates against U.S. dollar from 2000 to 2011 and found a linear combination among the exchange rates that has no conditional heteroscedasticity.     

Regularized receiver operating characteristic-based logistic regression for grouped variable selection with composite criterion

It is well known that statistical classifiers trained from imbalanced data lead to low true positive rates and select inconsistent significant variables. In this article, an improved method is proposed to enhance the classification accuracy for the minority class by differentiating misclassification cost for each group. The overall error rate is replaced by an alternative composite criterion. Furthermore, we propose an approach to estimate the tuning parameter, the composite criterion, and the cut-point simultaneously. Simulations show that the proposed method achieves a high true positive rate on prediction and a good performance on variable selection for both continuous and categorical predictors, even with highly imbalanced data. An illustrative example of the analysis of the suboptimal health state data in traditional Chinese medicine is discussed to show the reasonable application of the proposed method.     

A Statistical Model for Social Network Labeling

We consider a social network from which one observes not only network structure (i.e., nodes and edges) but also a set of labels (or tags, keywords) for each node (or user). These labels are self-created and closely related to the user’s career status, life style, personal interests, and many others. Thus, they are of great interest for online marketing. To model their joint behavior with network structure, a complete data model is developed. The model is based on the classical p₁ model but allows the reciprocation parameter to be label-dependent. By focusing on connected pairs only, the complete data model can be generalized into a conditional model. Compared with the complete data model, the conditional model specifies only the conditional likelihood for the connected pairs. As a result, it suffers less risk from model misspecification. Furthermore, because the conditional model involves connected pairs only, the computational cost is much lower. The resulting estimator is consistent and asymptotically normal. Depending on the network sparsity level, the convergence rate could be different. To demonstrate its finite sample performance, numerical studies (based on both simulated and real datasets) are presented.     

Probability Elicitation Under Severe Time Pressure: A Rank‐Based Method

Probability elicitation protocols are used to assess and incorporate subjective probabilities in risk and decision analysis. While most of these protocols use methods that have focused on the precision of the elicited probabilities, the speed of the elicitation process has often been neglected. However, speed is also important, particularly when experts need to examine a large number of events on a recurrent basis. Furthermore, most existing elicitation methods are numerical in nature, but there are various reasons why an expert would refuse to give such precise ratio‐scale estimates, even if highly numerate. This may occur, for instance, when there is lack of sufficient hard evidence, when assessing very uncertain events (such as emergent threats), or when dealing with politicized topics (such as terrorism or disease outbreaks). In this article, we adopt an ordinal ranking approach from multicriteria decision analysis to provide a fast and nonnumerical probability elicitation process. Probabilities are subsequently approximated from the ranking by an algorithm based on the principle of maximum entropy, a rule compatible with the ordinal information provided by the expert. The method can elicit probabilities for a wide range of different event types, including new ways of eliciting probabilities for stochastically independent events and low‐probability events. We use a Monte Carlo simulation to test the accuracy of the approximated probabilities and try the method in practice, applying it to a real‐world risk analysis recently conducted for DEFRA (the U.K. Department for the Environment, Farming and Rural Affairs): the prioritization of animal health threats.     

A Summary of Some Research on PC and Bayesian PC Criterion in China

In the past two decades, Pitman closeness (PC) criterion has been studied intensively in China. But many of research works were written in Chinese, which cannot be accessed by researchers from other countries. In this paper, we briefly summarize part of main results on the PC criterion in linear model in China. First, we present the basic model and some definitions. Then, we introduce the PC superiority for covariance adjustment estimate, and a class of biased estimates such as a kind of linear estimate, James–Stein estimate and the principal components estimate. Third, we introduce Bayesian PC superiorities for several different linear models such as ordinary univariate regression model, multivariate linear model and analysis of variance model. Finally, some results of robustness under Bayesian PC criterion are shown.     

Dealing with the biased effects issue when handling huge datasets: the case of INVALSI data

The increasing prevalence of huge datasets addresses the research to appropriate statistical methods for solving troubles caused by their complexity. On the one hand, several techniques are mentioned in the literature, especially for the time-consuming and variables reduction issues. On the other, less debate is devoted to the statistical inference issue. Indeed, a large number of involved statistical units may lead to wrongly consider as significant variables without any actual impact on the phenomenon under study. This paper suggests a suitable subsampling procedure for the reduction of the number of statistical units and provides a novel index for the assessment of the significance effects. The proposal is validated by comparing results obtained from the analysis on the original data to those obtained from the proposed subsampling approach. The illustrative application focuses on the educational dataset made available by the National Committee for the Evaluation of the Italian Education Systems (INVALSI). This dataset collects information about the student features and achievements in Maths within the lower secondary schools of the Lombardy region (Italy). Due to the hierarchical structure of the data, a multilevel model is implemented with the purpose of investigating the effects of both individual and school factors on student Maths score.     

A mixed poisson–inverse-gaussian regression model

The mixed Poisson–inverse-Gaussian distribution has been used by Holla, Sankaran, Sichel, and others in univariate problems involving counts. We propose a Poisson–inverse-Gaussian regression model which can be used for regression analysis of counts. The model provides an attractive framework for incorporating random effects in Poisson regression models and in handling extra-Poisson variation. Maximum-likelihood and quasilikelihood-moment estimation is investigated and illustrated with an example involving motor-insurance claims.