高校后勤社会化改革对策研究

高校后勤工作是教学工作、科研工作的基础和保障,其服务质量的高低直接影响着学校的全面健康发展。我国社会主义市场经济体制的建立完善和高等教育体制改革的不断深化,原有的福利型、公益型的后勤体制已不能适应社会主义市场经济体制的需要,我国高校后勤社会化改革已势在必行。针对高校后勤社会化改革的对策进行研究。     

试论电子商务物流的发展方向

物流是商务系统的重要组成部分,电子商务的快速发展同样依赖于物流体系的高效运作。我国物流体系的发展是快速且有成效的,但是却跟不上电子商务的增长速度,因而直接影响着电子商务的前进步伐。鉴于物流发展对电子商务的重要作用,本文提出电子商务物流应该按照现代化、信息化、柔性化和规范化的方向发展,以适应电子商务的发展。     

A new class of survival regression models with heavy-tailed errors: robustness and diagnostics

Birnbaum-Saunders models have largely been applied in material fatigue studies and reliability analyses to relate the total time until failure with some type of cumulative damage. In many problems related to the medical field, such as chronic cardiac diseases and different types of cancer, a cumulative damage caused by several risk factors might cause some degradation that leads to a fatigue process. In these cases, BS models can be suitable for describing the propagation lifetime. However, since the cumulative damage is assumed to be normally distributed in the BS distribution, the parameter estimates from this model can be sensitive to outlying observations. In order to attenuate this influence, we present in this paper BS models, in which a Student-t distribution is assumed to explain the cumulative damage. In particular, we show that the maximum likelihood estimates of the Student-t log-BS models attribute smaller weights to outlying observations, which produce robust parameter estimates. Also, some inferential results are presented. In addition, based on local influence and deviance component and martingale-type residuals, a diagnostics analysis is derived. Finally, a motivating example from the medical field is analyzed using log-BS regression models. Since the parameter estimates appear to be very sensitive to outlying and influential observations, the Student-t log-BS regression model should attenuate such influences. The model checking methodologies developed in this paper are used to compare the fitted models.     

Mixed linear regression with equi-cross-correlated errors

Received: August 12, 1999; revised version: April 3, 2000     

Expected estimating equations to accommodate covariate measurement error

Estimating equations which are not necessarily likelihood-based score equations are becoming increasingly popular for estimating regression model parameters. This paper is concerned with estimation based on general estimating equations when true covariate data are missing for all the study subjects, but surrogate or mismeasured covariates are available instead. The method is motivated by the covariate measurement error problem in marginal or partly conditional regression of longitudinal data. We propose to base estimation on the expectation of the complete data estimating equation conditioned on available data. The regression parameters and other nuisance parameters are estimated simultaneously by solving the resulting estimating equations. The expected estimating equation (EEE) estimator is equal to the maximum likelihood estimator if the complete data scores are likelihood scores and conditioning is with respect to all the available data. A pseudo-EEE estimator, which requires less computation, is also investigated. Asymptotic distribution theory is derived. Small sample simulations are conducted when the error process is an order 1 autoregressive model. Regression calibration is extended to this setting and compared with the EEE approach. We demonstrate the methods on data from a longitudinal study of the relationship between childhood growth and adult obesity.     

Marginal regression models with a time to event outcome and discrete multiple source predictors

Information from multiple informants is frequently used to assess psychopathology. We consider marginal regression models with multiple informants as discrete predictors and a time to event outcome. We fit these models to data from the Stirling County Study; specifically, the models predict mortality from self report of psychiatric disorders and also predict mortality from physician report of psychiatric disorders. Previously, Horton et al. found little relationship between self and physician reports of psychopathology, but that the relationship of self report of psychopathology with mortality was similar to that of physician report of psychopathology with mortality. Generalized estimating equations (GEE) have been used to fit marginal models with multiple informant covariates; here we develop a maximum likelihood (ML) approach and show how it relates to the GEE approach. In a simple setting using a saturated model, the ML approach can be constructed to provide estimates that match those found using GEE. We extend the ML technique to consider multiple informant predictors with missingness and compare the method to using inverse probability weighted (IPW) GEE. Our simulation study illustrates that IPW GEE loses little efficiency compared with ML in the presence of monotone missingness. Our example data has non-monotone missingness; in this case, ML offers a modest decrease in variance compared with IPW GEE, particularly for estimating covariates in the marginal models. In more general settings, e.g., categorical predictors and piecewise exponential models, the likelihood parameters from the ML technique do not have the same interpretation as the GEE. Thus, the GEE is recommended to fit marginal models for its flexibility, ease of interpretation and comparable efficiency to ML in the presence of missing data.     

A score test for overdispersion in Poisson regression based on the generalized Poisson-2 model

Overdispersion is a common phenomenon in Poisson modeling. The generalized Poisson (GP) regression model accommodates both overdispersion and underdispersion in count data modeling, and is an increasingly popular platform for modeling overdispersed count data. The Poisson model is one of the special cases in the collection of models which may be specified by GP regression. Thus, we may derive a test of overdispersion which compares the equi-dispersion Poisson model within the context of the more general GP regression model. The score test has an advantage over the likelihood ratio test (LRT) and over the Wald test in that the score test only requires that the parameter of interest be estimated under the null hypothesis (the Poisson model). Herein, we propose a score test for overdispersion based on the GP model (specifically the GP-2 model) and compare the power of the test with the LRT and Wald tests. A simulation study indicates the proposed score test based on asymptotic standard normal distribution is more appropriate in practical applications.     

Smoothing parameter selection for a class of semiparametric linear models

Summary.  Spline-based approaches to non-parametric and semiparametric regression, as well as to regression of scalar outcomes on functional predictors, entail choosing a parameter controlling the extent to which roughness of the fitted function is penalized. We demonstrate that the equations determining two popular methods for smoothing parameter selection, generalized cross-validation and restricted maximum likelihood, share a similar form that allows us to prove several results which are common to both, and to derive a condition under which they yield identical values. These ideas are illustrated by application of functional principal component regression, a method for regressing scalars on functions, to two chemometric data sets.     

Cross-cultural measurement equivalence of generalized trust. Evidence from the European Social Survey (2002 and 2004)

Generalized trust features as the most prominent attitudinal element of social capital, and as such the concept is widely used in comparative research. In this article we investigate the cross-cultural equivalence of the three-item scale on generalized trust that is included in the European Social Survey 2002 and 2004 waves. The use of metric equivalence tests demonstrates that these tree items can be considered as a reliable and cross-culturally valid concept. If we apply the stricter scalar equivalence test, however, the result is that the scale is not sufficiently equivalent across European societies, with especially the item on expecting help from others showing major problems in a number of countries. We close this article by offering some suggestions on how the generalized trust scale might be used in a (sufficiently) reliable manner in cross-cultural research across Europe.

制造业与物流业联动机理研究

针对目前物流业成为制造业发展的主要瓶颈因素,致使制造业与物流业不能更好地联动发展的问题,通过对宏观角度的制造业与上下游企业之间的物流连接和微观角度的制造企业供产销三方面物流进行深入分析,研究制造业与物流业联动机理,为实现制造业和物流业之间的相互融合、相互促进以及共同发展提供理论支持,并简要提出制造业与物流业联动发展的战略规划。