A Method to Handle Zero Counts in the Multinomial Model

In the context of an objective Bayesian approach to the multinomial model, Dirichlet(a, …, a) priors with a < 1 have previously been shown to be inadequate in the presence of zero counts, suggesting that the uniform prior (a = 1) is the preferred candidate. In the presence of many zero counts, however, this prior may not be satisfactory either. A model selection approach is proposed, allowing for the possibility of zero parameters corresponding to zero count categories. This approach results in a posterior mixture of Dirichlet distributions and marginal mixtures of beta distributions, which seem to avoid the problems that potentially result from the various proposed Dirichlet priors, in particular in the context of extreme data with zero counts.     

学习迁移的系统理论:PPEE理论模型的建构和意义

学习是人类社会永恒的主题。为了更好地利用过去的经验，增强学习效果，个人和组织进行学习迁移具有重要意义。学习迁移是指将在以往通过学习或解决问题所获得的经验，用于解决之后所遇到的问题。本文力图建立系统的学习迁移理论，研究人、问题、环境的因素对于学习迁移的影响。本文采用理论推导的研究方法，在前人研究的基础上，提出相应的理论模型。本文运用系统的观点，构建了学习迁移的PPEE理论模型，认为人的因素、问题的因素、人和问题的交互作用、以及环境的调节作用，会共同影响学习迁移的效果。本理论主要从五个方面论述学习迁移的机制。首先，将人的因素按照不同层面划分为后端基本能力、中端心理过程、前端知识结构，讨论了其各自对学习迁移效果的直接影响。其次，将问题的因素划分为形象相似性和抽象相似性，并根据这两个维度构建了四种问题间关系类型，并探究了其与学习迁移效果之间的关系。第三，同时考虑人的因素和问题的因素，研究了二者之间的互动对于学习迁移效果的影响。第四，将环境因素划分为时间紧迫性和空间重要性两个维度，探讨了人和环境，以及问题和环境之间的互动对学习迁移效果的影响。第五，基于系统整体的方面，研究人、问题、环境三者之间的互动对学习迁移效果的交互影响。本文还认为，该模型不仅适用于个人，也同样适用于团队、组织、地区、甚至国家和社会等各个层面，影响各个层面主体的学习迁移。本文最后总结了研究的理论和实践贡献，并提出了未来研究课题。本文对丰富学习理论、特别是学习迁移理论，以及推动教育和管理实践的发展进步具有重要的意义。     

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.     

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。     

Bootstrap order selection for SETAR models

In this paper, we investigate the selecting performances of a bootstrapped version of the Akaike information criterion for nonlinear self-exciting threshold autoregressive-type data generating processes. Empirical results will be obtained via Monte Carlo simulations. The quality of our method is assessed by comparison with its non-bootstrap counterpart and through a novel procedure based on artificial neural networks.     

Ranking and estimation of related means in two—way models—a Bayesian approach

A hierarchical Bayesian approach to ranking and selection as well as estimation of related means in two—way models is considered. Using the method of Monte Carlo simulation with importance sampling, we are able to carry out efficiently the three or four dimensional integrations as needed. An example is included to illustrate the methodology.