Norms,status and the dynamics of advice networks: A case study

The issue of the influence of norms on behavior is as old as sociology itself. This paper explores the effect of normative homophily (i.e. “sharing the same normative choices”) on the evolution of the advice network among lay judges in a courthouse. 0020 and 0025 social exchange theory suggests that members select advisors based on the status of the advisor. Additional research shows that members of an organization use similarities with others in ascribed, achieved or inherited characteristics, as well as other kinds of ties, to mitigate the potentially negative effects of this strong status rule. We elaborate and test these theories using data on advisor choice in the Commercial Court of Paris. We use a jurisprudential case about unfair competition (material and “moral” damages), a case that we submitted to all the judges of this court, to test the effect of normative homophily on the selection of advisors, controlling for status effects. Normative homophily is measured by the extent to which two judges are equally “punitive” in awarding damages to plaintiffs. Statistical analyses combine longitudinal advice network data collected among the judges with their normative dispositions. Contrary to what could be expected from conventional sociological theories, we find no pure effect of normative homophily on the choice of advisors. In this case, therefore, sharing the same norms and values does not have, by itself, a mitigating effect and does not contribute to the evolution of the network. We argue that status effects, conformity and alignments on positions of opinion leaders in controversies still provide the best insights into the relationship between norms, structure and behavior.     

Transductive versions of the LASSO and the Dantzig Selector

Transductive methods are useful in prediction problems when the training dataset is composed of a large number of unlabeled observations and a smaller number of labeled observations. In this paper, we propose an approach for developing transductive prediction procedures that are able to take advantage of the sparsity in the high dimensional linear regression. More precisely, we define transductive versions of the LASSO (Tibshirani, 1996) and the Dantzig Selector (Candès and Tao, 2007). These procedures combine labeled and unlabeled observations of the training dataset to produce a prediction for the unlabeled observations. We propose an experimental study of the transductive estimators that shows that they improve the LASSO and Dantzig Selector in many situations, and particularly in high dimensional problems when the predictors are correlated. We then provide non-asymptotic theoretical guarantees for these estimation methods. Interestingly, our theoretical results show that the Transductive LASSO and Dantzig Selector satisfy sparsity inequalities under weaker assumptions than those required for the “original” LASSO.     

Model Checks in Inverse Regression Models with Convolution‐Type Operators

Abstract. We consider the problem of testing parametric assumptions in an inverse regression model with a convolution‐type operator. An L ₂ ‐type goodness‐of‐fit test is proposed which compares the distance between a parametric and a non‐parametric estimate of the regression function. Asymptotic normality of the corresponding test statistic is shown under the null hypothesis and under a general non‐parametric alternative with different rates of convergence in both cases. The feasibility of the proposed test is demonstrated by means of a small simulation study. In particular, the power of the test against certain types of alternative is investigated. Finally, an empirical example is provided, in which the proposed methods are applied to the determination of the shape of the luminosity profile of the elliptical galaxy NGC 5017.     

Quantile regression and variable selection for the single-index model

In this paper, we propose a new full iteration estimation method for quantile regression (QR) of the single-index model (SIM). The asymptotic properties of the proposed estimator are derived. Furthermore, we propose a variable selection procedure for the QR of SIM by combining the estimation method with the adaptive LASSO penalized method to get sparse estimation of the index parameter. The oracle properties of the variable selection method are established. Simulations with various non-normal errors are conducted to demonstrate the finite sample performance of the estimation method and the variable selection procedure. Furthermore, we illustrate the proposed method by analyzing a real data set.     

Partners,Institutions and International Currencies: The International Political Foundations for the Rise of the Renminbi

The use of an international currency is not just a natural market phenomenon; it relies heavily on international political foundations. The historical lessons of the four major international currencies—the pound, dollar, euro and yen—show that a strong partner network and a favorable international institutional environment are the two major political foundations for the formation of a stable international currency. The rise of the renminbi, as a late starter looking to become a major international currency, depends not only on the requisite economic conditions but on governmental diplomacy aimed at attracting more monetary cooperation partners and creating international institutions that support its worldwide use. This strategy reinforces the political foundations for the rise of the renminbi. Building a network of partners supporting the renminbi as an international currency and creating a system of international monetary institutions are the real tests of the issuing country’s political leadership. This political leadership capacity has three dimensions: public trust founded on reputation building; a centripetal force driven by provision of benefits; and coercive force applied through sanctions. Comparatively speaking, the renminbi is still in the early stage of its ascent, so the development of its political infrastructure should concentrate mainly on gaining public trust through reputation-building and employing centripetal force involving provision of benefits.     

Comparison of procedures for fitting the autoregressive order of a vector error correction model

This paper investigates the lag length selection problem of a vector error correction model by using a convergent information criterion and tools based on the Box–Pierce methodology recently proposed in the literature. The performances of these approaches for selecting the optimal lag length are compared via Monte Carlo experiments. The effects of misspecified deterministic trend or cointegrating rank on the lag length selection are studied. Noting that processes often exhibit nonlinearities, the cases of iid and conditionally heteroscedastic errors will be considered. Strategies that can avoid misleading situations are proposed.     

Variable selection for semiparametric proportional hazards model under progressive Type-II censoring

Variable selection is an effective methodology for dealing with models with numerous covariates. We consider the methods of variable selection for semiparametric Cox proportional hazards model under the progressive Type-II censoring scheme. The Cox proportional hazards model is used to model the influence coefficients of the environmental covariates. By applying Breslow’s “least information” idea, we obtain a profile likelihood function to estimate the coefficients. Lasso-type penalized profile likelihood estimation as well as stepwise variable selection method are explored as means to find the important covariates. Numerical simulations are conducted and Veteran’s Administration Lung Cancer data are exploited to evaluate the performance of the proposed method.     

澜湄命运共同体建设的意义、动因和路径选择

构建澜湄国家命运共同体是中国周边外交和亚洲命运共同体构想的具体实践，将助力东盟共同体建设，促进次区域稳定和繁荣。本文通过对澜湄命运共同体建设的意义、动因和影响因素分析，提出了澜湄命运共同体建设的路径选择。     

Bayesian analysis of multivariate stochastic volatility with skew return distribution

Multivariate stochastic volatility models with skew distributions are proposed. Exploiting Cholesky stochastic volatility modeling, univariate stochastic volatility processes with leverage effect and generalized hyperbolic skew t-distributions are embedded to multivariate analysis with time-varying correlations. Bayesian modeling allows this approach to provide parsimonious skew structure and to easily scale up for high-dimensional problem. Analyses of daily stock returns are illustrated. Empirical results show that the time-varying correlations and the sparse skew structure contribute to improved prediction performance and Value-at-Risk forecasts.     

问题类型划分方法视野下的犯罪概念研究

在问题类型划分方法的视野下,犯罪概念问题应该属于纯粹刑法学问题中的解释选择问题,但刑法学界以往有关犯罪概念的讨论却大多将其作为刑法问题中的价值判断问题。由于未能妥当确定犯罪概念的问题类型,从而使学界有关犯罪概念的讨论未能达成最低限度的学术共识。作为纯粹刑法学问题中的解释选择问题,形式与实质相结合的混合犯罪概念不存在被替代的必要性,而犯罪概念也不应被规定在刑法典中。