考虑项目关联关系的R&D网络风险传播建模与仿真

考虑同一企业内不同项目间关联关系的情况下，基于研发项目的数量将研发网络分为不同子网络。借鉴SIS模型构建了R&D网络风险传播动力学模型，并对该模型进行了数理解析与仿真分析。结果表明：R&D网络风险传播阈值是由不同子网络的拓扑结构以及企业内不同项目间的关联程度共同决定的；考虑项目关联的研发网络风险传播阈值小于不考虑关联关系的研发网络风险传播阈值；项目间关联关系的企业个数及项目间的关联程度对风险在不同子网络上的传播速度与传播范围均具有影响。该研究成果对于提高研发网络抗风险能力具有重要意义。     

Providing a secure base for LGBTQ young people in foster care: The role of foster carers

The experiences and needs of lesbian, gay, bisexual, trans and queer/questioning (LGBTQ) young people in care have been overlooked in England, in both policy and research. This paper reports on findings from the first study of LGBTQ young people in care in England and focuses on the nature of foster carers' experiences and perspectives on caring for LGBTQ young people. Qualitative interviews regarding the fostering role in caring for LGBTQ young people were conducted with a sample of foster carers (n = 26) and analysed thematically. Foster carers described the importance of offering LGBTQ young people not only the nurturing relationships that all children in care need but also availability, sensitivity and acceptance to help young people manage stigma and other challenges associated with minority sexual orientation and gender identity. The Secure Base caregiving model provided a framework for analysing the different dimensions of these relationships. Understanding caregiving roles and relationships for LGBTQ young people in care has important implications for recruiting, training, matching and supporting foster carers to care for LGBTQ young people effectively.     

The Eurasian Economic Union: Development Prospects and Possible Obstacles

This article analyzes the development prospects and problems for the Eurasian Economic Union (EAEU). It examines issues of integration within the EAEU, as well as interactions with other countries, both CIS (Commonwealth of Independent States) and non-CIS. The main problem of integration within the EAEU is the dominance of redistributive motives over productive ones. This article assesses the extent of Russian oil and gas transfers to EAEU partners and the impact of tax maneuvers on their size. It shows that the creation of mechanisms for redistributing profits within the EAEU will allow a positive economic effect to materialize from free trade agreements with non-CIS countries. The article assesses the risks for the EAEU related to Russia’s introduction of a unilateral ban on food imports from countries on the sanctions list and to the possible establishment of tariffs on trade with Ukraine.     

Optimal designs for multivariate logistic mixed models with longitudinal data

This paper considers the optimal design problem for multivariate mixed-effects logistic models with longitudinal data. A decomposition method of the binary outcome and the penalized quasi-likelihood are used to obtain the information matrix. The D-optimality criterion based on the approximate information matrix is minimized under different cost constraints. The results show that the autocorrelation coefficient plays a significant role in the design. To overcome the dependence of the D-optimal designs on the unknown fixed-effects parameters, the Bayesian D-optimality criterion is proposed. The relative efficiencies of designs reveal that both the cost ratio and autocorrelation coefficient play an important role in the optimal designs.     

A general central limit theorem and a subsampling variance estimator for α‐mixing point processes

We establish a central limit theorem for multivariate summary statistics of nonstationary α‐mixing spatial point processes and a subsampling estimator of the covariance matrix of such statistics. The central limit theorem is crucial for establishing asymptotic properties of estimators in statistics for spatial point processes. The covariance matrix subsampling estimator is flexible and model free. It is needed, for example, to construct confidence intervals and ellipsoids based on asymptotic normality of estimators. We also provide a simulation study investigating an application of our results to estimating functions.     

Influence diagnostics for the structural sharpe model under normal/independent distributions

A method is proposed in this paper to assess the local influence of minor perturbations for the Sharpe model when the normal distribution is replaced by normal/independent (NI) distributions. The family of NI distributions is an attractive class of symmetric heavy-tailed densities that includes as special cases the normal, t-Student, slash, and the contaminated normal distributions. Since the returns of the market are not observable, the statistical analysis is carried out in the context of an errors-in-variables model. An influence analysis for detecting influential observations (atypical returns) is developed to investigate the sensitivity of the maximum likelihood estimators. Diagnostic measures are obtained based on the conditional expectation of the complete-data log-likelihood function. The results are illustrated by using a set of shares of companies traded in the Chilean stock market.     

A Graphical Tool for Interpreting Regression Coefficients of Trinomial Logit Models

Multinomial logit (also termed multi-logit) models permit the analysis of the statistical relation between a categorical response variable and a set of explicative variables (called covariates or regressors). Although multinomial logit is widely used in both the social and economic sciences, the interpretation of regression coefficients may be tricky, as the effect of covariates on the probability distribution of the response variable is nonconstant and difficult to quantify. The ternary plots illustrated in this article aim at facilitating the interpretation of regression coefficients and permit the effect of covariates (either singularly or jointly considered) on the probability distribution of the dependent variable to be quantified. Ternary plots can be drawn both for ordered and for unordered categorical dependent variables, when the number of possible outcomes equals three (trinomial response variable); these plots allow not only to represent the covariate effects over the whole parameter space of the dependent variable but also to compare the covariate effects of any given individual profile. The method is illustrated and discussed through analysis of a dataset concerning the transition of master’s graduates of the University of Trento (Italy) from university to employment.     

Regression analysis of case-cohort studies in the presence of dependent interval censoring

The case-cohort design is widely used as a means of reducing the cost in large cohort studies, especially when the disease rate is low and covariate measurements may be expensive, and has been discussed by many authors. In this paper, we discuss regression analysis of case-cohort studies that produce interval-censored failure time with dependent censoring, a situation for which there does not seem to exist an established approach. For inference, a sieve inverse probability weighting estimation procedure is developed with the use of Bernstein polynomials to approximate the unknown baseline cumulative hazard functions. The proposed estimators are shown to be consistent and the asymptotic normality of the resulting regression parameter estimators is established. A simulation study is conducted to assess the finite sample properties of the proposed approach and indicates that it works well in practical situations. The proposed method is applied to an HIV/AIDS case-cohort study that motivated this investigation.