A semiparametric approach for modelling multivariate nonlinear time series

In this article, a semiparametric time‐varying nonlinear vector autoregressive (NVAR) model is proposed to model nonlinear vector time series data. We consider a combination of parametric and nonparametric estimation approaches to estimate the NVAR function for both independent and dependent errors. We use the multivariate Taylor series expansion of the link function up to the second order which has a parametric framework as a representation of the nonlinear vector regression function. After the unknown parameters are estimated by the maximum likelihood estimation procedure, the obtained NVAR function is adjusted by a nonparametric diagonal matrix, where the proposed adjusted matrix is estimated by the nonparametric kernel estimator. The asymptotic consistency properties of the proposed estimators are established. Simulation studies are conducted to evaluate the performance of the proposed semiparametric method. A real data example on short‐run interest rates and long‐run interest rates of United States Treasury securities is analyzed to demonstrate the application of the proposed approach. The Canadian Journal of Statistics 47: 668–687; 2019 © 2019 Statistical Society of Canada     

The effect of small sample on optimal designs for logistic regression models

Asymptotic methods are commonly used in statistical inference for unknown parameters in binary data models. These methods are based on large sample theory, a condition which may be in conflict with small sample size and hence leads to poor results in the optimal designs theory. In this paper, we apply the second order expansions of the maximum likelihood estimator and derive a matrix formula for the mean square error (MSE) to obtain more precise optimal designs based on the MSE. Numerical results indicate the new optimal designs are more efficient than the optimal designs based on the information matrix.     

Model selection for factor analysis: Some new criteria and performance comparisons

This paper derives Akaike information criterion (AIC), corrected AIC, the Bayesian information criterion (BIC) and Hannan and Quinn’s information criterion for approximate factor models assuming a large number of cross-sectional observations and studies the consistency properties of these information criteria. It also reports extensive simulation results comparing the performance of the extant and new procedures for the selection of the number of factors. The simulation results show the di?culty of determining which criterion performs best. In practice, it is advisable to consider several criteria at the same time, especially Hannan and Quinn’s information criterion, Bai and Ng’s IC_p2 and BIC₃, and Onatski’s and Ahn and Horenstein’s eigenvalue-based criteria. The model-selection criteria considered in this paper are also applied to Stock and Watson’s two macroeconomic data sets. The results differ considerably depending on the model-selection criterion in use, but evidence suggesting five factors for the first data and five to seven factors for the second data is obtainable.     

Closure property of consistently varying random variables based on precise large deviation principles

Let X = {X₁, X₂, …} be a sequence of independent but not necessarily identically distributed random variables, and let η be a counting random variable independent of X. Consider randomly stopped sum S_η = ∑^η_{k = 1}X_k and random maximum S_(η) ? max?{S₀, …, S_η}. Assuming that each X_k belongs to the class of consistently varying distributions, on the basis of the well-known precise large deviation principles, we prove that the distributions of S_η and S_(η) belong to the same class under some mild conditions. Our approach is new and the obtained results are further studies of Kizinevi?, Sprindys, and ?iaulys (2016) and Andrulyt?, Manstavi?ius, and ?iaulys (2017).     

On approximations via convolution-defined mixture models

An often-cited fact regarding mixing or mixture distributions is that their density functions are able to approximate the density function of any unknown distribution to arbitrary degrees of accuracy, provided that the mixing or mixture distribution is sufficiently complex. This fact is often not made concrete. We investigate and review theorems that provide approximation bounds for mixing distributions. Connections between the approximation bounds of mixing distributions and estimation bounds for the maximum likelihood estimator of finite mixtures of location-scale distributions are reviewed.     

基于Mo-RVIKOR的混合多属性决策方法

针对复杂性和不确定性多属性决策问题,考虑定量和定性融合的属性形式,提出了模块化随机多准则妥协解排序法（Modular Random VlseKriterijumska Opti-mizacija I Kompromisno Resenje,Mo-RVIKOR）,该方法无需将信息统一,就能处理多种信息形式存在的多属性决策问题。采用精确数、随机变量处理定量评价信息,用概率语义术语集处理定性评价信息;通过改进离差最大化法确定属性权重;根据Mo-RVIKOR对决策对象进行排序;最后以某公司C2B定制化服务质量评测项目为例,验证了所提方法的有效性。     

大学英语教材评估标准探讨

教材在大学英语教学体系中处于中枢地位,是教学大纲的具体化以及教学内容的重要依托。近年来,随着英语教学改革的深化与发展,国内教材市场空前繁荣,各种英语教材和辅导材料充斥市场。如何选择合适的大学英语教材,充分发挥其在外语教学中的作用至关重要。文章在国内外学者研究的基础上,结合《大学英语课程教学要求（试行）》对大学英语课程的任务和总体目标,从教材的教学指导思想、设计与组织、内容、教学法、教师参考书等方面拟定了一个大学英语教材评估标准。     

合同法附随义务违约责任归责原则浅析

我国《合同法》第42、43条规定了缔约过失责任,第60条规定了合同履行过程中的附随义务,第92条规定了后合同义务。附随义务理论在我国《合同法》中被肯定下来,是学说继受和法典继受的结果,然而要使其真正衣土化,在我国生根成长,尚有很长的路要走。特别是,违反合同履行过程中的附随义务和后合同义务的归责原则,现行法律没有作出明确规定,给司法实践带来了困难。从法律规定附随义务的立法初衷等因素考虑,违反合同法附随义务的违约责任采用过错责任归责原则和过错推定的举证方式具有合理性。     

德性：行政管理的重要资源

德性是社会的道德规范内化为人的内心信念和内在准则的优良品质。行政人员内含的德性是行政管理的重要资源,当前则表现出稀缺性的特点。为确保行政管理中德性资源供给与需求间的动态平衡,可通过社会建构和个体自塑两种途径对德性资源进行培育。     

从归责原则角度论知识产权保护

知识产权的保护需要从侵权责任的角度进行规制,立法的完善应当从概念的掌握入手,了解侵权责任归责原则的确切含义,才可以讨论知识产权侵权责任的归责原则,才能够在这一原则的指导下解决各项知识产权侵权案件上的疑惑,以期完善侵权责任法下的知识产权保护。