吸收能力、研发合作创新激励与补贴政策

本文在前人的研究基础上建立了一个具有吸收能力的非合作研究与发展博弈模型,比较了有和没有吸收能力情况下的R&D投资影响,结果表明有吸收能力的企业R&D投资要更高,接着探讨了产品替代与互补关系下的技术溢出选择对产业的影响和社会最优选择的R&D投资水平及其补贴激励政策。     

夫妻一方举债案件中的当事人确定和嗣后救济

“夫妻共同债务”与 “连带债务”在债务发生原因和债务履行上存在差别,对其进行诉讼时,诉讼形式应为固有的必要共同诉讼。当债权人仅起诉名义举债人一方时,可能构成诉讼被告不适格。借鉴德日诉讼要件理论,法院可以对当事人适格问题进行“职权调查”,但所采用的诉讼资料应遵循“辩论主义”。在此基础之上,应明确法院的释明权适用情形,即通过对已经参加诉讼的当事人进行释明来改变诉的不合法状态。若因法院在此审判阶段释明权适用不到位,而最终不合法的诉获得了无法顺利执行的实体判决,债权人想要获得圆满救济仅能依靠再审。     

接点治理：论精准扶贫过程中的“村民评议”——对湘西B村的个案研究

作为深入乡土社会的“事件”,精准扶贫触发了国家政权和乡土社会之间的分歧。在数字化治理技术与乡土生活模糊性之间、国家正式权力与农村内生秩序之间、“区分逻辑”与乡土社会“平均主义”之间,两者存在明显差异。面对分歧,农村基层在精准扶贫过程中采取“村民评议”制度形成“接点治理”格局。研究发现,村民评议形成的接点治理主要包括以下治理机制：以熟人社会构成治理的信息机制,以多元主体参与构成治理的权威机制,以协商讨论构成治理的合法化机制。这一制度创造了新型制度场域和治理接点,有利于实现国家政权与乡土社会之间的融合。推进对湘西B村“村民评议”的个案分析,发掘其中的治理机制,完善其中存在的不足,实现从接点分离到接点融合,推进基层社会的有效治理。     

The nested joint clustering via Dirichlet process mixture model

This article focuses on the clustering problem based on Dirichlet process (DP) mixtures. To model both time invariant and temporal patterns, different from other existing clustering methods, the proposed semi-parametric model is flexible in that both the common and unique patterns are taken into account simultaneously. Furthermore, by jointly clustering subjects and the associated variables, the intrinsic complex shared patterns among subjects and among variables are expected to be captured. The number of clusters and cluster assignments are directly inferred with the use of DP. Simulation studies illustrate the effectiveness of the proposed method. An application to wheal size data is discussed with an aim of identifying novel temporal patterns among allergens within subject clusters.     

学分绩点制在武汉七所部属高校的实践与探索

学分绩点制是一种能客观衡量学生学习质量的教学管理制度。学分绩点制在武汉地区七校联合办学实践中的应用,为联合办学的可持续发展提供了有益的探索。     

侵权法中的作为义务探究

国外立法例对侵权法中作为义务的规制模式不尽相同,中国台湾地区和大陆地区理论界对作为义务的研究在结合中国司法实践方面略显不足。对于侵权法中作为义务的探究,应当将国外立法例、中国理论界的研究成果与司法实践情况结合起来。中国侵权法中作为义务的设计应当遵循如下思路:无须在一般条款中明确作为义务;合同约定不应成为侵权法中作为义务的来源;不应当规定一般救助义务;作为义务来源应当具有层级性,且该层级性应当以重塑安全保障义务为中心。     

Time series models with asymmetric Laplace innovations

We propose autoregressive moving average (ARMA) and generalized autoregressive conditional heteroscedastic (GARCH) models driven by asymmetric Laplace (AL) noise. The AL distribution plays, in the geometric-stable class, the analogous role played by the normal in the alpha-stable class, and has shown promise in the modelling of certain types of financial and engineering data. In the case of an ARMA model we derive the marginal distribution of the process, as well as its bivariate distribution when separated by a finite number of lags. The calculation of exact confidence bands for minimum mean-squared error linear predictors is shown to be straightforward. Conditional maximum likelihood-based inference is advocated, and corresponding asymptotic results are discussed. The models are particularly suited for processes that are skewed, peaked, and leptokurtic, but which appear to have some higher order moments. A case study of a fund of real estate returns reveals that AL noise models tend to deliver a superior fit with substantially less parameters than normal noise counterparts, and provide both a competitive fit and a greater degree of numerical stability with respect to other skewed distributions.     

“赔礼道歉”的异化与回归

我国《侵权责任法》延续了《民法通则》的规定,将赔礼道歉作为侵权责任承担方式之一种。从赔礼道歉责任承担方式在我国的产生来看,其有一定的历史时代原因;但在现代法的背景之下,该种责任承担方式不仅在理论上争议颇多,而且造成了司法实践中问题重重,其作为一种责任承担方式的合理性值得怀疑,将其从法律责任还原为道德责任实属必要。     

Bayesian bridge-randomized penalized quantile regression estimation for linear regression model with AP(q) perturbation

Bridge penalized regression has many desirable statistical properties such as unbiasedness, sparseness as well as ‘oracle’. In Bayesian framework, bridge regularized penalty can be implemented based on generalized Gaussian distribution (GGD) prior. In this paper, we incorporate Bayesian bridge-randomized penalty and its adaptive version into the quantile regression (QR) models with autoregressive perturbations to conduct Bayesian penalization estimation. Employing the working likelihood of the asymmetric Laplace distribution (ALD) perturbations, the Bayesian joint hierarchical models are established. Based on the mixture representations of the ALD and generalized Gaussian distribution (GGD) priors of coefficients, the hybrid algorithms based on Gibbs sampler and Metropolis-Hasting sampler are provided to conduct fully Bayesian posterior estimation. Finally, the proposed Bayesian procedures are illustrated by some simulation examples and applied to a real data application of the electricity consumption.     

Bayesian density regression for discrete outcomes

We develop Bayesian models for density regression with emphasis on discrete outcomes. The problem of density regression is approached by considering methods for multivariate density estimation of mixed scale variables, and obtaining conditional densities from the multivariate ones. The approach to multivariate mixed scale outcome density estimation that we describe represents discrete variables, either responses or covariates, as discretised versions of continuous latent variables. We present and compare several models for obtaining these thresholds in the challenging context of count data analysis where the response may be over‐ and/or under‐dispersed in some of the regions of the covariate space. We utilise a nonparametric mixture of multivariate Gaussians to model the directly observed and the latent continuous variables. The paper presents a Markov chain Monte Carlo algorithm for posterior sampling, sufficient conditions for weak consistency, and illustrations on density, mean and quantile regression utilising simulated and real datasets.