Markov Properties for Acyclic Directed Mixed Graphs

We consider acyclic directed mixed graphs, in which directed edges ( x → y ) and bi-directed edges ( x ↔ y ) may occur. A simple extension of Pearl's d -separation criterion, called m -separation, is applied to these graphs. We introduce a local Markov property which is equivalent to the global property resulting from the m -separation criterion for arbitrary distributions.     

一类非线性混合整规划的无约束连续化解法

将整系数多项式 0 - 1混合整规划问题化成无约束多项式规划问题。通过解该问题 ,能得到原问题的近似解。处理方法的特点是能够直接处理不等式约束情形 ,而不需要先将不等式约束化成等式约束再来处理     

论刑罚个别化原则的根基和属性——一种多视角的分析与考察

近年我国刑法学界,刑罚个别化原则正日益引起学者的广泛关注和深入研究.刑罚个别化原则有其内在的合理根基它在方法论基础上得益于近代犯罪学对刑法学的推动;在伦理内涵方面体现了人性关怀;在价值追求中实现了公正圆满.刑罚个别化原则不仅仅是一个量刑原则或刑法原则,而且是一个重要的刑事政策原则.     

异种自由刑数罪并罚规则研究

异种自由刑数罪并罚是在原有数罪并罚规则基础之上,针对多样化的犯罪现象与司法适用而设置的规则。根据我国现行《刑法》规定的异种自由刑并罚内容,吸收原则与并科原则各自蕴含着合理性依据。然而立足于整体数罪并罚的规则,此种规定却存在二者相互矛盾以及刑罚轻重的冲突,给理论研究与实践适用带来困扰。基于此,通过对域外异种自由刑数罪并罚规则的考察,借鉴其中折抵和限制加重原则的内容,提出我国异种自由刑并罚规则的改进建议:对有期徒刑和拘役刑的并罚采用综合折抵原则与限制加重原则的方式,而对有期徒刑、拘役刑和管制刑的并罚继续采用并科原则,从而更好地实现数罪并罚的适用。     

Multilevel maximum likelihood estimation with application to covariance matrices

The asymptotic variance of the maximum likelihood estimate is proved to decrease when the maximization is restricted to a subspace that contains the true parameter value. Maximum likelihood estimation allows a systematic fitting of covariance models to the sample, which is important in data assimilation. The hierarchical maximum likelihood approach is applied to the spectral diagonal covariance model with different parameterizations of eigenvalue decay, and to the sparse inverse covariance model with specified parameter values on different sets of nonzero entries. It is shown computationally that using smaller sets of parameters can decrease the sampling noise in high dimension substantially.     

Quadratic Approximation via the SCAD Penalty with a Diverging Number of Parameters

The high-dimensional data arises in diverse fields of sciences, engineering and humanities. Variable selection plays an important role in dealing with high dimensional statistical modelling. In this article, we study the variable selection of quadratic approximation via the smoothly clipped absolute deviation (SCAD) penalty with a diverging number of parameters. We provide a unified method to select variables and estimate parameters for various of high dimensional models. Under appropriate conditions and with a proper regularization parameter, we show that the estimator has consistency and sparsity, and the estimators of nonzero coefficients enjoy the asymptotic normality as they would have if the zero coefficients were known in advance. In addition, under some mild conditions, we can obtain the global solution of the penalized objective function with the SCAD penalty. Numerical studies and a real data analysis are carried out to confirm the performance of the proposed method.     

我国环境刑罚轻缓化探析

毋庸置疑,在整个国际社会对环境犯罪处以轻缓刑罚的背景下,我国亦应该对环境刑罚采用轻缓化的原则。诚然,我国的环境刑罚制度与其他国家相比还有许多亟待完善的地方,但这并不妨碍我们在借鉴其他国家先进的立法经验的基础上构建适合我国具体国情的环境刑罚制度。在环境刑罚中要突出自由刑的适用,扩大并完善财产刑的适用,明确财产刑中过于模糊的规定,逐步改善环境犯罪的刑事处罚措施过于单一的缺点和功能上的不足,同时提高对辅助刑罚措施的关注。在环境犯罪方面,在适用传统刑罚措施惩治的基础上配合适用辅助刑罚措施,使我国环境刑罚轻缓化实践不至于成为一纸空谈。     

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.     

Multivariate normal estimation: the case (n < p)

Estimation in the multivariate context when the number of observations available is less than the number of variables is a classical theoretical problem. In order to ensure estimability, one has to assume certain constraints on the parameters. A method for maximum likelihood estimation under constraints is proposed to solve this problem. Even in the extreme case where only a single multivariate observation is available, this may provide a feasible solution. It simultaneously provides a simple, straightforward methodology to allow for specific structures within and between covariance matrices of several populations. This methodology yields exact maximum likelihood estimates.     

Conditional covariance penalties for mixed models

The prediction error for mixed models can have a conditional or a marginal perspective depending on the research focus. We introduce a novel conditional version of the optimism theorem for mixed models linking the conditional prediction error to covariance penalties for mixed models. Different possibilities for estimating these conditional covariance penalties are introduced. These are bootstrap methods, cross-validation, and a direct approach called Steinian. The behavior of the different estimation techniques is assessed in a simulation study for the binomial-, the t-, and the gamma distribution and for different kinds of prediction error. Furthermore, the impact of the estimation techniques on the prediction error is discussed based on an application to undernutrition in Zambia.