心理资本:组织竞争优势之源

从心理资本的概念与构成维度、心理资本的测量与干预、心理资本的影响效应研究三个方面对心理资本做了阐述。在对国内外现有文献进行回顾和评述的基础上,指出现有研究的不足,提出了对未来的展望。     

我国大米产业波动的来源及冲击路径

通过采用修正后的两变量结构向量自回归模型考察我国大米产业波动来源,并利用脉冲响应函数分析供求冲击对大米价格和产出的传导路径。结果发现:大米产业70%的价格波动来源于供给冲击的作用;短期内供给冲击占据产出波动的主导地位,而长期内需求冲击解释54%左右的价格波动;总体而言,供给冲击在中国大米价格波动中具有主导作用。因此,政府应对大米产业波动及时调控,防止由于政策调控的滞后效应,导致价格与产出波动放大的结果。     

基于ARMA模型的江苏省城乡居民信息消费差距及其预测分析

在1998—2012年江苏省城乡居民信息消费数据的基础上,利用信息消费的倾向、信息消费的系数、信息消费结构等指标对比分析江苏省城乡居民信息消费的现状,发现城乡居民信息消费虽呈现持续增长的良好态势,但两者的差距也显而易见。故通过构建ARMA模型,对两者的差距进行拟合并作出短期预测,实证结果表明城乡居民间的"数字鸿沟"有愈演愈烈的趋势。基于此,在维持城镇居民信息消费示范作用的前提下,应从提升农民信息消费能力、构建完善的农村信息消费环境等方面努力缩小城乡之间的"数字鸿沟"。     

粮食补贴政策对农户非农就业时间的影响分析——基于安徽省天长市的调研数据

采用对安徽省天长市农户的实地调查数据,运用Tobit模型分析粮食补贴政策对农户非农就业时间的影响。结果表明:仅考虑单个粮食补贴政策变量的情况下,粮食补贴金额对农户非农就业时间具有显著的负向影响,提高粮食补贴金额在一定程度上会减少农户非农就业时间供给;在考虑多变量的情况下,粮食补贴金额对农户非农就业时间的负向影响并不显著,主要原因是受教育程度较高、家庭务工子女数较多、家庭劳动力数量较多的农户对粮食补贴资金的刺激性不太敏感以及粮食补贴金额对农户农业收入的贡献有限;不管是否考虑所有变量,农户对补贴方式的满意度评价对农户非农就业时间都具有显著的负向影响,农户对补贴方式的满意度越高,其减少非农就业时间的可能性就越大。提出要充分发挥粮食补贴政策的积极作用,应进一步完善补贴金额、补贴方式,提高农户满意度。     

基于校企深度融合的“职场式”人才培养模式研究——以某校市场营销专业为例

市场对营销人才的需求日益呈现出专业化、差异化和个性化趋势。因此,高校市场营销专业须创新人才培养模式以满足不同企业对多元人才之需求。以某高校市场营销专业为例,校企双方通过共同培养人才、以赛促合、教师服务于企业等校企深度融合的方式,创建了"职场式"人才培养模式。这种兼顾学生、学校和企业三方利益共赢的人才培养模式,较好地解决了市场营销专业人才培养与市场需求脱节的问题。此人才培养模式对其他高校市场营销专业的人才培养具有一定的借鉴意义。     

Dose‐Response Models Incorporating Aerosol Size Dependency for Francisella tularensis

The effect of bioaerosol size was incorporated into predictive dose‐response models for the effects of inhaled aerosols of Francisella tularensis (the causative agent of tularemia) on rhesus monkeys and guinea pigs with bioaerosol diameters ranging between 1.0 and 24 μm. Aerosol‐size‐dependent models were formulated as modification of the exponential and β‐Poisson dose‐response models and model parameters were estimated using maximum likelihood methods and multiple data sets of quantal dose‐response data for which aerosol sizes of inhaled doses were known. Analysis of F. tularensis dose‐response data was best fit by an exponential dose‐response model with a power function including the particle diameter size substituting for the rate parameter k scaling the applied dose. There were differences in the pathogen's aerosol‐size‐dependence equation and models that better represent the observed dose‐response results than the estimate derived from applying the model developed by the International Commission on Radiological Protection (ICRP, 1994) that relies on differential regional lung deposition for human particle exposure.     

A new stratified three-stage unrelated randomized response model for estimating a rare sensitive attribute based on the Poisson distribution

This article suggests an efficient method of estimating a rare sensitive attribute which is assumed following Poisson distribution by using three-stage unrelated randomized response model instead of the Land et al. model (2011 Land, M., S. Singh, and S. A. Sedory. 2011. Estimation of a rare sensitive attribute using poisson distribution. Statistics 46 (3):351–60. doi:10.1080/02331888.2010.524300.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) when the population consists of some different sized clusters and clusters selected by probability proportional to size(:pps) sampling. A rare sensitive parameter is estimated by using pps sampling and equal probability two-stage sampling when the parameter of a rare unrelated attribute is assumed to be known and unknown.

We extend this method to the case of stratified population by applying stratified pps sampling and stratified equal probability two-stage sampling. An empirical study is carried out to show the efficiency of the two proposed methods when the parameter of a rare unrelated attribute is assumed to be known and unknown.     

Model Uncertainty and Bayesian Model Averaged Benchmark Dose Estimation for Continuous Data

The benchmark dose (BMD) approach has gained acceptance as a valuable risk assessment tool, but risk assessors still face significant challenges associated with selecting an appropriate BMD/BMDL estimate from the results of a set of acceptable dose‐response models. Current approaches do not explicitly address model uncertainty, and there is an existing need to more fully inform health risk assessors in this regard. In this study, a Bayesian model averaging (BMA) BMD estimation method taking model uncertainty into account is proposed as an alternative to current BMD estimation approaches for continuous data. Using the “hybrid” method proposed by Crump, two strategies of BMA, including both “maximum likelihood estimation based” and “Markov Chain Monte Carlo based” methods, are first applied as a demonstration to calculate model averaged BMD estimates from real continuous dose‐response data. The outcomes from the example data sets examined suggest that the BMA BMD estimates have higher reliability than the estimates from the individual models with highest posterior weight in terms of higher BMDL and smaller 90th percentile intervals. In addition, a simulation study is performed to evaluate the accuracy of the BMA BMD estimator. The results from the simulation study recommend that the BMA BMD estimates have smaller bias than the BMDs selected using other criteria. To further validate the BMA method, some technical issues, including the selection of models and the use of bootstrap methods for BMDL derivation, need further investigation over a more extensive, representative set of dose‐response data.     

A Diversity Index for Model Space Selection in the Estimation of Benchmark and Infectious Doses via Model Averaging

In chemical and microbial risk assessments, risk assessors fit dose‐response models to high‐dose data and extrapolate downward to risk levels in the range of 1–10%. Although multiple dose‐response models may be able to fit the data adequately in the experimental range, the estimated effective dose (ED) corresponding to an extremely small risk can be substantially different from model to model. In this respect, model averaging (MA) provides more robustness than a single dose‐response model in the point and interval estimation of an ED. In MA, accounting for both data uncertainty and model uncertainty is crucial, but addressing model uncertainty is not achieved simply by increasing the number of models in a model space. A plausible set of models for MA can be characterized by goodness of fit and diversity surrounding the truth. We propose a diversity index (DI) to balance between these two characteristics in model space selection. It addresses a collective property of a model space rather than individual performance of each model. Tuning parameters in the DI control the size of the model space for MA.