文化产业数字化内生生长与外生协同创新的进化机理研究

数字经济与国家文化新基建的发展驱动了以数字文化体验为代表的文化消费新范式,因而产生了革新文化消费渠道和场景、创新智能文化装备制造、打通异构文化数据资源库、完善数字化公共文化服务等亟待解决的产业发展新需求。文化产业数字化内生生长要求立足产业内部视角,打造文化产业供给侧改革、文化智能装备制造、文化创作开发、文化数字化治理、文化创客协同等五大引擎。文化产业的外生协同需在文化消费营销创新、文化大数据平台建设和产业融合发展等多点发力,要开展跨界、跨学科、跨组织的循环互动,构建文化产业数字化内外双循环驱动的创新机制,灵活应对数字化时代的新挑战。     

语言多属性决策的目标规划模型

研究了只有部分属性权重信息、属性值以语言变量或不确定语言变量形式给出且决策者对方案有偏好信息的语言多属性决策问题.给出了语言变量和不确定语言变量的运算法则,以及不确定语言变量之间比较的可能度公式,定义了语言变量的偏离度概念.在属性值以1)语言变量和2)不确定语言变量,这两种形式给出的情形下,分别建立了一个基于偏离度的目标规划模型,并通过求解这两种模型分别获得相应的属性权重.然后对于情形1),利用语言加权平均(LWA)算子,对语言决策信息进行加权集成,继而对方案进行排序和择优;对于情形2),利用不确定语言加权平均(ULWA)算子,对不确定语言决策信息进行加权集成,并利用可能度公式构造可能度矩阵(互补判断矩阵),继而利用互补判断矩阵排序公式对决策方案进行排序和择优.最后进行了实例分析.     

企业战略联盟风险防范体系的架构研究

在对企业战略联盟存在的风险及规避措施进行理论透析的基础上,给出了企业战略联盟伙伴选择机制的权值排序模型。提出了企业战略联盟的3种风险防范整合架构,并论述了三者之间的相关关系。最后通过实例对企业战略联盟伙伴选择机制的权值排序模型和风险防范整合体系进行了验证。     

优先级债务与次级债务的激励效应

把通货膨胀率和红利支付率融入优先级债务和次级债务的激励效应模型. 利用期权的 对策论分析方法,给出了优先级债务、次级债务、股票和公司价值的解析评价公式,并分析了通 货膨胀率和红利支付对公司的破产决策、次债务的发行决策,以及对股东和债权人之间财富转 移的重要影响. 说明了通胀率和红利支付率在实际借债合同中是不能被忽略的因素.     

Population structure and correlation between auxiliary and target variables may affect precision of estimates in forest inventory

Simulations of forest inventory in several populations compared simple random with “quick probability proportional to size” (QPPS) sampling. The latter may be applied in the absence of a list sampling frame and/or prior measurement of the auxiliary variable. The correlation between the auxiliary and target variables required to render QPPS sampling more efficient than simple random sampling varied over the range 0.3–0.6 and was lower when sampling from populations that were skewed to the right. Two possible analytical estimators of the standard error of the estimate of the mean for QPPS sampling were found to be less reliable than bootstrapping.     

Marginal likelihood estimation from the Metropolis output: tips and tricks for efficient implementation in generalized linear latent variable models

The marginal likelihood can be notoriously difficult to compute, and particularly so in high-dimensional problems. Chib and Jeliazkov employed the local reversibility of the Metropolis–Hastings algorithm to construct an estimator in models where full conditional densities are not available analytically. The estimator is free of distributional assumptions and is directly linked to the simulation algorithm. However, it generally requires a sequence of reduced Markov chain Monte Carlo runs which makes the method computationally demanding especially in cases when the parameter space is large. In this article, we study the implementation of this estimator on latent variable models which embed independence of the responses to the observables given the latent variables (conditional or local independence). This property is employed in the construction of a multi-block Metropolis-within-Gibbs algorithm that allows to compute the estimator in a single run, regardless of the dimensionality of the parameter space. The counterpart one-block algorithm is also considered here, by pointing out the difference between the two approaches. The paper closes with the illustration of the estimator in simulated and real-life data sets.     

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.     

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.     

Complex Latent Variable Modeling in Educational Assessment

Bayesian item response theory models have been widely used in different research fields. They support measuring constructs and modeling relationships between constructs, while accounting for complex test situations (e.g., complex sampling designs, missing data, heterogenous population). Advantages of this flexible modeling framework together with powerful simulation-based estimation techniques are discussed. Furthermore, it is shown how the Bayes factor can be used to test relevant hypotheses in assessment using the College Basic Academic Subjects Examination (CBASE) data.