实物资金流量表的预测方法研究

资金流量表是国民经济核算体系中的重要组成部分。然而，由于在编制过程中需要采集大量的数据，通常情况下，很多国家的资金流量表都会有较长时间的滞后。在编制实物资金流量表的延长表时，已有方法通常是基于基期与预测期交易收支结构保持不变的假定条件。然而，经济结构发生显著变化时，该类方法就会失效。基于上述问题，研究弱化模型的假设条件，并提出了新的实物资金流量表预测方法( 简称 FPTF方法)。根据表中元素必须满足的约束条件，该方法通过建立数学模型解除约束，其次基于历史数据的动态趋势，采用适当的时间序列分析方法来预测目标年份的实物资金流量表。通过仿真分析，验证了所提方法的有效性和稳定性。此外，基于中国1992年～2014 年的实物资金流量表数据进行实例分析，取得了满意的分析结果。     

Using Lean Six Sigma to improve mobile order fulfilment process in a telecom service sector

This study proposes and follows a specific and systematic framework for implementing Lean Six Sigma (LSS) methodology in a telecom company in order to improve customer satisfaction by minimizing the company’s response time to customer requirements. The goal of this study was achieved by utilizing several LSS tools under five phases of the DMAIC methodology. Unlike previous studies in the telecom sector that used only qualitative method, in this study, both qualitative and quantitative methods were utilized to draw meaningful conclusions. As a result of the implementation of the LSS methodology, the average order fulfilment lead time for sales orders (SO) and value-added service (VAS) orders was reduced from 10.3 to 5.9 days and from 1.5 to 0.5 days, respectively. The reduction in lead time resulted in an increase in the sigma level for SO and VAS orders from 0.44 to 1.26 and from 0.73 to 2.66, respectively. These improvements were expected to lead to a financial benefit in savings of over $600,000 per year in operational costs, enhancements to customer experience and an increase in revenue generating opportunities. Moreover, this article enriches the existing literature on the application of LSS concept in the service industry, and helps the company to speed up the response to customer requirements.     

Translating cross-lagged effects into incidence rates and risk ratios: The case of psychosocial safety climate and depression

Longitudinal studies are the gold standard of empirical work and stress research whenever experiments are not plausible. Frequently, scales are used to assess risk factors and their consequences, and cross-lagged effects are estimated to determine possible risks. Methods to translate cross-lagged effects into risk ratios to facilitate risk assessment do not yet exist, which creates a divide between psychological and epidemiological work stress research. The aim of the present paper is to demonstrate how cross-lagged effects can be used to assess the risk ratio of different levels of psychosocial safety climate (PSC) in organisations, an important psychosocial risk for the development of depression. We used available longitudinal evidence from the Australian Workplace Barometer (N?=?1905) to estimate cross-lagged effects of PSC on depression. We applied continuous time modelling to obtain time-scalable cross effects. These were further investigated in a 4-year Monte Carlo simulation, which translated them into 4-year incident rates. Incident rates were determined by relying on clinically relevant 2-year periods of depression. We suggest a critical value of PSC?=?26 (corresponding to ?1.4 SD), which is indicative of more than 100% increased incidents of persistent depressive disorder in 4-year periods compared to average levels of PSC across 4 years.     

Re‐framing the Analysis: A 3‐dimensional Perspective of Prisoners’ Children's Well‐being

This article highlights three dimensions to understanding children's well‐being during and after parental imprisonment which have not been fully explored in current research. A consideration of ‘time’ reveals the importance of children's past experiences and their anticipated futures. A focus on ‘space’ highlights the impact of new or altered environmental dynamics. A study of ‘agency’ illuminates how children cope within structural, material and social confines which intensify vulnerability and dependency. This integrated perspective reveals important differences in individual children's experiences and commonalities in broader systemic and social constraints on prisoners’ children. The paper analyses data from a prospective longitudinal study of 35 prisoners’ children during and after their (step) father's imprisonment to illustrate the arguments.     

The ‘right time’ – negotiating the timing of interviews with vulnerable young people

Participant retention is a key factor in determining the success of longitudinal research. Challenges in re-locating and retaining participants over the long term are major issues for researchers working with young people who face adversity and experience frequent changes in circumstances. This article reports on a study of vulnerable young people and their transition into adulthood. Rather than the more conventional schedule-based approach to locating and re-interviewing young people, a relational process, the ‘right time’ framework, was used to facilitate young people's involvement in the study. Embedded in the ‘right time’ framework is recognition of the diverse and fluctuating circumstances that shape young people's availability for interviews. Several case examples are considered which amplify the way that the ‘right time’ framework allowed the research to navigate around these circumstances. The case examples highlight the value young people attached to being involved in the research, the influence on the ‘right time’ of wider relational tensions for young people and the need to negotiate researcher status as a different sort of adult. The ‘right time’ framework contributed to a high retention rate in the study generating a more representative sample and enhancing the subsequent data analysis by providing valuable insights into the lives of these vulnerable young people.     

Efficient Estimation in Heteroscedastic Partially Linear Varying Coefficient Models

This article considers statistical inference for the heteroscedastic partially linear varying coefficient models. We construct an efficient estimator for the parametric component by applying the weighted profile least-squares approach, and show that it is semiparametrically efficient in the sense that the inverse of the asymptotic variance of the estimator reaches the semiparametric efficiency bound. Simulation studies are conducted to illustrate the performance of the proposed method.     

Inference and optimal design of multiple constant-stress testing for generalized half-normal distribution under type-II progressive censoring

The generalized half-normal (GHN) distribution and progressive type-II censoring are considered in this article for studying some statistical inferences of constant-stress accelerated life testing. The EM algorithm is considered to calculate the maximum likelihood estimates. Fisher information matrix is formed depending on the missing information law and it is utilized for structuring the asymptomatic confidence intervals. Further, interval estimation is discussed through bootstrap intervals. The Tierney and Kadane method, importance sampling procedure and Metropolis-Hastings algorithm are utilized to compute Bayesian estimates. Furthermore, predictive estimates for censored data and the related prediction intervals are obtained. We consider three optimality criteria to find out the optimal stress level. A real data set is used to illustrate the importance of GHN distribution as an alternative lifetime model for well-known distributions. Finally, a simulation study is provided with discussion.     

消费者导向类型与电商全渠道决策研究

从现实出发，基于不同消费者导向类型研究了电商的全渠道决策问题。首先建立电商单渠道销售的基础模型，并得出最优产品组合、价格和交付时间。在此基础上，给出了消费者为产品导向型和渠道导向型情况下电商采取全渠道决策的条件，得出双渠道产品组合、价格及交付时间的最优决策；并进行了数值验证。研究发现，同一渠道提供的产品其最优价格相同，最优交付时间只和消费者耐心程度及交付成本函数有关。若消费者为产品导向型，仅当双渠道运营成本之差较小且消费者耐心程度较低时，电商进入线下渠道才有利可图，且在线下渠道提供最受欢迎的产品，线上渠道提供剩余产品。若消费者为渠道导向型，电商进入线下渠道必然有利可图，且在线下渠道提供最受欢迎的产品，线上渠道提供所有产品。     

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.     

基于股价预测的泛证券投资组合策略

本文基于期望效用最大化和L₁-中位数估计研究了在线投资组合选择问题。与EG（Exponential Gradient）策略仅利用单期价格信息估计价格趋势不同，本文将利用多期价格信息估计价格趋势，以提高在线策略的性能。首先，基于多期价格数据，利用L₁-中位数估计得到预期价格趋势。然后，通过期望效用最大化，提出一个新的具有线型时间复杂度的在线策略，EGLM（Exponential Gradient via L₁-Median）。并通过相对熵函数定义资产权重向量的距离，进而证明了EGLM策略具有泛证券投资组合性质。最后，利用国内外6个证券市场的历史数据进行实证分析，结果表明相较于UP（Universal Portfolio）策略和EG策略，EGLM策略有更好的竞争性能。