企业信息门户的信息构建

企业信息门户是企业信息交流的重要平台,通过信息构建的企业信息门户可以更为有效地利用企业的信息资源。通过对企业信息门户和信息构建的介绍,论述了企业信息门户建设的重点是信息构建,在信息构建理念的基础上对企业信息门户的建设提出了一套建设思路。     

计算机信息技术课程教学质量保证体系的建立与实践

为提高教学质量,借鉴ISO9000企业贯标与认证的思想,根据教育部相关指导文件的要求,建立了计算机信息技术课程教学质量保证体系,并依据该体系的要求,在课程的教材、题库、教学手段和方法以及考试方式等方面加强了建设与改进。     

非同步交易与信息传导：中美股市关系研究

 中美股市之间存在非同步交易问题,直接利用收盘价建模会得到偏误的结果,因此本文利用隔夜收益率和开市收益率对非同步交易下的中美股市信息传导模式进行研究。研究发现,两国股市开市收益率变化均表现出波动聚集特征,而且均只在开盘时对另一方市场的交易信息作出反应,其中,我国股市开盘时受到的美国股市的影响,大大强于我国股市对其开盘的影响;并且随着次贷危机的发生,这种影响愈加明显。但中美两国股市开市交易期间的相互影响均不显著,不存在收益和波动性的信息传导关系。     

The cross-country and cross-time measurement invariance of positive and negative affect scales: Evidence from European Social Survey

Survey-based measures of subjective well-being are increasingly often analyzed cross-culturally. However, international comparison of these measures requires measurement invariance. Therefore, the major goal of this study is to investigate the cross-country and cross-time comparability of the 4-item positive and 7-item negative affect scales used in European Social Survey Round 3 (2006) and Round 6 (2012). This study applies both the traditional exact and the more recent Bayesian approximate approach to assess whether the affect scales are measurement invariant. The approximate approach detected several non-invariant items that are problematic for cross-national comparison and should be dropped from the scales. Consequently, measurement invariance was established in all countries over the two rounds for the reduced scales, allowing researchers to meaningfully compare their latent mean scores and the relationships with other theoretical constructs of interest. Thus, the study highlights the advantages of using multiple indicators and the necessity of measurement invariance testing in subjective well-being research.     

Evaluation of Bayesian multiple stage estimation under spatial CAR model variants

In this study, an evaluation of Bayesian hierarchical models is made based on simulation scenarios to compare single-stage and multi-stage Bayesian estimations. Simulated datasets of lung cancer disease counts for men aged 65 and older across 44 wards in the London Health Authority were analysed using a range of spatially structured random effect components. The goals of this study are to determine which of these single-stage models perform best given a certain simulating model, how estimation methods (single- vs. multi-stage) compare in yielding posterior estimates of fixed effects in the presence of spatially structured random effects, and finally which of two spatial prior models – the Leroux or ICAR model, perform best in a multi-stage context under different assumptions concerning spatial correlation. Among the fitted single-stage models without covariates, we found that when there is low amount of variability in the distribution of disease counts, the BYM model is relatively robust to misspecification in terms of DIC, while the Leroux model is the least robust to misspecification. When these models were fit to data generated from models with covariates, we found that when there was one set of covariates – either spatially correlated or non-spatially correlated, changing the values of the fixed coefficients affected the ability of either the Leroux or ICAR model to fit the data well in terms of DIC. When there were multiple sets of spatially correlated covariates in the simulating model, however, we could not distinguish the goodness of fit to the data between these single-stage models. We found that the multi-stage modelling process via the Leroux and ICAR models generally reduced the variance of the posterior estimated fixed effects for data generated from models with covariates and a UH term compared to analogous single-stage models. Finally, we found the multi-stage Leroux model compares favourably to the multi-stage ICAR model in terms of DIC. We conclude that the mutli-stage Leroux model should be seriously considered in applications of Bayesian disease mapping when an investigator desires to fit a model with both fixed effects and spatially structured random effects to Poisson count data.     

Spatial smoothing of low birth weight rate in Bangladesh using Bayesian hierarchical model

The term low birth weight refers an event where a newborn baby has a weight that is less than 2500?g. This is an essential indicator while the interest is in public health issues such as infant mortality, maternal complications, and antenatal care, etc. of a country, particularly, for a developing country like Bangladesh. The regional development programs are in the current priority list of Bangladesh government and other policy makers. Many of such regional development programs may need the spatial distribution of relative risk for low birth weight that can be obtained by mapping the risks over small area domains like the districts of Bangladesh. This study aims to find whether is there any spatial dependence among the relative risks of low birth weight for the districts of Bangladesh. This has been investigated using Moran's I statistic and a significant spatial dependence in the relative risks was found. Then, attempt has been made to rediscover the spatial distribution based on the idea of spatial smoothing. A Bayesian hierarchical model is used considering percent received antenatal care and female labor force participation as covariates to smooth the observed relative risks of low birth weight in 64 districts of Bangladesh. Revised spatial distribution taking the spatial dependence under consideration through intrinsic conditional autoregressive model is derived and showed in choropleth map along with its different behaviors.     

Model‐free causal inference of binary experimental data

For binary experimental data, we discuss randomization‐based inferential procedures that do not need to invoke any modeling assumptions. In addition to the classical method of moments, we also introduce model‐free likelihood and Bayesian methods based solely on the physical randomization without any hypothetical super population assumptions about the potential outcomes. These estimators have some properties superior to moment‐based ones such as only giving estimates in regions of feasible support. Due to the lack of identification of the causal model, we also propose a sensitivity analysis approach that allows for the characterization of the impact of the association between the potential outcomes on statistical inference.     

Bayesian quantile regression and variable selection for partial linear single-index model: Using free knot spline

Partial linear single-index model (PLSIM) has both the flexibility of nonparametric treatment and interpretability of linear term, yet existing literatures about it mainly focused on mean regression, and quantile regression analysis is scarce. Based on free knot spline approximation, we apply asymmetric Laplace distribution to implement Bayesian quantile regression, and perform variable selection in linear term and index vector via binary indicators. Our approach is exempt from regularity conditions in frequentist method, and could execute variable selection and quantile regression under mutual posterior correction, which is also the first work to implement them jointly for PLSIM in fully Bayesian framework. The numerical simulation manifests the superiority of our approach to previous methods, which embodied in better efficiency of variable selection, index vector estimates and link function approximation with different error distributions. For illustration of its application, we build a power consumption model of A₂/O process in wastewater treatment and emphatically analyze the impact of water quality factors.     

Welfare gains of the poor: An endogenous Bayesian approach with spatial random effects

We introduce a Bayesian instrumental variable procedure with spatial random effects that handles endogeneity, and spatial dependence with unobserved heterogeneity. We find through a limited Monte Carlo experiment that our proposal works well in terms of point estimates and prediction. We apply our method to analyze the welfare effects generated by a process of electricity tariff unification on the poorest households. In particular, we deduce an Equivalent Variation measure where there is a budget constraint for a two-tiered pricing scheme, and find that 10% of the poorest municipalities attained welfare gains above 2% of their initial income.     

A generalized Bayesian nonlinear mixed‐effects regression model for zero‐inflated longitudinal count data in tuberculosis trials

In this paper, we investigate Bayesian generalized nonlinear mixed‐effects (NLME) regression models for zero‐inflated longitudinal count data. The methodology is motivated by and applied to colony forming unit (CFU) counts in extended bactericidal activity tuberculosis (TB) trials. Furthermore, for model comparisons, we present a generalized method for calculating the marginal likelihoods required to determine Bayes factors. A simulation study shows that the proposed zero‐inflated negative binomial regression model has good accuracy, precision, and credibility interval coverage. In contrast, conventional normal NLME regression models applied to log‐transformed count data, which handle zero counts as left censored values, may yield credibility intervals that undercover the true bactericidal activity of anti‐TB drugs. We therefore recommend that zero‐inflated NLME regression models should be fitted to CFU count on the original scale, as an alternative to conventional normal NLME regression models on the logarithmic scale.