中介机构参与下联盟企业知识转移的三方利益博弈分析

知识转移是企业保存知识、提升创新能力的重要途径，中介机构在转移过程中发挥了重要作用。本文以联盟企业间知识转移为研究对象，考虑了企业的不同心理压力，首先建立了联盟企业双方的讨价还价博弈模型；其次构建了考虑中介机构参与知识转移的博弈模型，分析和讨论三方收益变化的影响因素及策略选择。结果表明，在直接知识转移博弈中，转移主体的收益大小与自身的心理压力呈负相关，而与对方的心理压力呈正相关，同等心理压力下知识转出方占优；中介机构参与博弈情形下，中介机构作为协作角色的博弈收益仅受博弈双方心理压力影响，且与知识接收方协作可获得更大收益，而主导角色博弈情形下收益受到三方主体心理压力的综合影响，并在主导知识接收博弈时获得收益更大；中介机构的角色选择与知识转出方心理压力变化相关性不明显，主要受到知识接收方及自身心理压力影响。     

Random effects regression mixtures for analyzing infant habituation

Random effects regression mixture models are a way to classify longitudinal data (or trajectories) having possibly varying lengths. The mixture structure of the traditional random effects regression mixture model arises through the distribution of the random regression coefficients, which is assumed to be a mixture of multivariate normals. An extension of this standard model is presented that accounts for various levels of heterogeneity among the trajectories, depending on their assumed error structure. A standard likelihood ratio test is presented for testing this error structure assumption. Full details of an expectation-conditional maximization algorithm for maximum likelihood estimation are also presented. This model is used to analyze data from an infant habituation experiment, where it is desirable to assess whether infants comprise different populations in terms of their habituation time.     

Multivariate normal mean-variance mixture distribution based on Lindley distribution

This article introduces a new asymmetric distribution constructed by assuming the multivariate normal mean-variance mixture model. Called normal mean-variance mixture of the Lindley distribution, we derive some mathematical properties of the new distribution. Also, a feasible maximum likelihood estimation procedure using the EM algorithm and the asymptotic standard errors of parameter estimates are developed. The performance of the proposed distribution is illustrated by means of real datasets and simulation analysis.     

Time‐change models for asymmetric processes

Many records in environmental sciences exhibit asymmetric trajectories. The physical mechanisms behind these records may lead for example to sample paths with different characteristics at high and low levels (up–down asymmetries) or in the ascending and descending phases leading to time irreversibility (front–back asymmetries). Such features are important for many applications, and there is a need for simple and tractable models that can reproduce them. In this paper, we explore original time‐change models where the clock is a stochastic process that depends on the observed trajectory. The ergodicity of the proposed model is established under general conditions, and this result is used to develop nonparametric estimation procedures based on the joint distribution of the process and its derivative. The methodology is illustrated on meteorological and oceanographic data sets. We show that, combined with a marginal transformation, the proposed methodology is able to reproduce important characteristics of the data set such as marginal distributions, up‐crossing intensity, and up–down and front–back asymmetries.     

Bayesian nonparametric modelling of the link function in the single-index model using a Bernstein–Dirichlet process prior

This paper proposes the use of the Bernstein–Dirichlet process prior for a new nonparametric approach to estimating the link function in the single-index model (SIM). The Bernstein–Dirichlet process prior has so far mainly been used for nonparametric density estimation. Here we modify this approach to allow for an approximation of the unknown link function. Instead of the usual Gaussian distribution, the error term is assumed to be asymmetric Laplace distributed which increases the flexibility and robustness of the SIM. To automatically identify truly active predictors, spike-and-slab priors are used for Bayesian variable selection. Posterior computations are performed via a Metropolis-Hastings-within-Gibbs sampler using a truncation-based algorithm for stick-breaking priors. We compare the efficiency of the proposed approach with well-established techniques in an extensive simulation study and illustrate its practical performance by an application to nonparametric modelling of the power consumption in a sewage treatment plant.     

Skew-normal factor analysis models with incomplete data

Traditional factor analysis (FA) rests on the assumption of multivariate normality. However, in some practical situations, the data do not meet this assumption; thus, the statistical inference made from such data may be misleading. This paper aims at providing some new tools for the skew-normal (SN) FA model when missing values occur in the data. In such a model, the latent factors are assumed to follow a restricted version of multivariate SN distribution with additional shape parameters for accommodating skewness. We develop an analytically feasible expectation conditional maximization algorithm for carrying out parameter estimation and imputation of missing values under missing at random mechanisms. The practical utility of the proposed methodology is illustrated with two real data examples and the results are compared with those obtained from the traditional FA counterparts.     

Nonparametric multiple expectile regression via ER-Boost

Expectile regression [Newey W, Powell J. Asymmetric least squares estimation and testing, Econometrica. 1987;55:819–847] is a nice tool for estimating the conditional expectiles of a response variable given a set of covariates. Expectile regression at 50% level is the classical conditional mean regression. In many real applications having multiple expectiles at different levels provides a more complete picture of the conditional distribution of the response variable. Multiple linear expectile regression model has been well studied [Newey W, Powell J. Asymmetric least squares estimation and testing, Econometrica. 1987;55:819–847; Efron B. Regression percentiles using asymmetric squared error loss, Stat Sin. 1991;1(93):125.], but it can be too restrictive for many real applications. In this paper, we derive a regression tree-based gradient boosting estimator for nonparametric multiple expectile regression. The new estimator, referred to as ER-Boost, is implemented in an R package erboost publicly available at http://cran.r-project.org/web/packages/erboost/index.html. We use two homoscedastic/heteroscedastic random-function-generator models in simulation to show the high predictive accuracy of ER-Boost. As an application, we apply ER-Boost to analyse North Carolina County crime data. From the nonparametric expectile regression analysis of this dataset, we draw several interesting conclusions that are consistent with the previous study using the economic model of crime. This real data example also provides a good demonstration of some nice features of ER-Boost, such as its ability to handle different types of covariates and its model interpretation tools.     

基于不对称需求信息的供应链最小订购量决策

在由一个供应商和一个零售商组成的两级分散供应链中,供应商通过制定最小订购量取得规模效应,保障自身利益.当零售商和供应商之间存在需求信息不对称时,即零售商掌握需求信息而供应商仅知道需求信息中价格敏感因子的分布,如何进行最小订购量决策成为供应商面临的一个重要问题.针对这一问题,从营销视角构建了基于Stackelberg博弈的利润最大化模型.假设供应商知道需求的价格敏感因子服从正态分布,通过严密的数学推导确定了模型中的最优最小订购量.将提出的最优最小订购量决策方法应用于云存储的销售供应链中,确定了云存储供应商销售的最优最小存储容量,阐释了方法的合理性与有效性.通过实验研究发现,最小订购量的设置提升了供应商的利润.所提方法对于考虑最小订购量的供应链协调研究具有积极的推动作用.     

逆向选择条件下带奖惩金的两期保险契约模型及其Pareto改进性研究

为了更有效的规避影响保险市场交易效率的逆向选择问题，本文分投保人风险类型为两种和多种情形建立了带奖惩金的两期保险契约模型，首次提出可以用奖励金和惩罚金有效甄别投保人的风险类型。该模型根据投保人第一个保险期内的索赔情况在第二个保险期对其进行奖励或惩罚，高风险类型的投保人如果选择为低风险类型投保人设计的保险契约，则其在第二阶段受到惩罚的概率要远远大于得到奖励的概率，即风险越高的投保人越害怕惩罚金，因此所建模型满足斯彭斯-莫里斯分离条件。带奖惩金的两期保险契约模型中保险公司的期望利润仍然为0，并不会给投保人带来额外的经济负担，却能够实现对传统部分保险契约简单重复两次的严格帕累托改进。最后采用一个算例说明了该模型的有效性。     

非对称基坑开挖监测位移下数值对比分析

以南京市青奥轴线地下交通工程主隧道基坑非对称开挖水平位移监测数据为依据,对水平位移监测值与ABAQUS数值模拟值进行对比分析。得出结论:墙体水平位移首先是悬臂开挖的墙顶向外发生三角形分布的位移,然后随着支撑的架设,墙体发生转动,数值模拟值与实际监测值基本符合;在开挖面附近的土压力,随着墙体高度的增加而增大;在支撑以上部分,模拟值要小于计算值,而在开挖面以下部分,则模拟值大于计算值;随着悬臂段开挖深度的增加,悬臂段最大土压力值也在逐渐增加,墙底土压力值在逐渐减小。