从分税制到项目制:制度演进和组织机制

分税制改革以后,中国随之进行了公共预算体制改革,由此形成了一种新的国家治理体制——项目制。地方政府的项目支出主要有三种类型：上级专项转移支付、上级非补助性项目支出和本级项目支出。三类项目支出在地方得以汇聚和重组,在很大程度上形塑了基层政府的财政结构。项目制本质上不是对科层制的一种摆脱或超越,而是国家主动对政府科层体系的一次完善和补充,是近代国家政权建设在新时期的延续与拓展。项目制的直接目的是“硬化”预算约束,深层目的是增强政府的回应能力,二者在实践中呈现一定张力,政府治理的理性化和技术化并不必然增强其对公共需求的回应能力,甚至可能形成反向效应。     

应用型本科电气信息类专业的实践性教学研究

电气信息类专业具有多学科有机融合的特点，应用型本科电气信息类专业面向生产实际和工程技术开发培养具有较强实践能力和一定创新精神的高级工程技术人才，专业的实践性教学十分重要。德国大学(Uni)和高等专业学院(Fachhochschule)电气信息类专业的实践性教学包括课程练习、实验、工业实践训练、项目研究、毕业论文等形式，形成了完整的体系。与德国相比，我国应用型本科电气信息类专业的实践性教学体系尚在形成之中。独立开设实验课、加强基础实验、开发综合性实验、现场实习、建设校内实践教学基地以充分利用仿真技术是应用型本科电气信息类专业加强实践性教学的有效途径。     

以公共部门绩效评估为基点的评估类型比较

在整个公共管理领域,公共部门绩效评估与公共项目绩效评估、公共人力资源绩效评估有联系又有区别。此外,同样作为一种工具与手段,公共部门绩效评估与企业绩效评估亦有可比的地方。了解各种评估类型之间的相互关系,对于公共部门绩效评估的发展大有裨益。     

A note on the equality of the OLSE and the BLUE of the parametric function in the general Gauss–Markov model

In this note we consider the equality of the ordinary least squares estimator (OLSE) and the best linear unbiased estimator (BLUE) of the estimable parametric function in the general Gauss–Markov model. Especially we consider the structures of the covariance matrix V for which the OLSE equals the BLUE. Our results are based on the properties of a particular reparametrized version of the original Gauss–Markov model.      

Characterization of Unacceptable Solutions in LP Discriminant Analysis*

Many linear programming models have been proposed for performing discriminant analysis. Partial characterizations for unacceptable solutions have been presented and new models proposed to circumvent these problems. In this paper those conditions leading to unacceptable solutions for all two-group models are characterized.     

Inference in a Binary Response Model with Applications to Data Analysis*

Standard errors of the coefficients of a logistic regression (a binary response model) based on the asymptotic formula are compared to those obtained from the bootstrap through Monte Carlo simulations. The computer intensive bootstrap method, a nonparametric alternative to the asymptotic estimate, overestimates the true value of the standard errors while the asymptotic formula underestimates it. However, for small samples the bootstrap estimates are substantially closer to the true value than their counterpart derived from the asymptotic formula. The methodology is discussed using two illustrative data sets. The first example deals with a logistic model explaining the log-odds of passing the ERA amendment by the 1982 deadline as a function of percent of women legislators and the percent vote for Reagan. In the second example, the probability that an ingot is ready to roll is modelled using heating time and soaking time as explanatory variables. The results agree with those obtained from the simulations. The value of the study to better decision making through accurate statistical inference is discussed.     

A NEW CLASS OF MODELS FOR THE DISCRIMINANT PROBLEM

Discriminant analysis is relevant to business decision making in a variety of contexts, such as when one decides to make or buy a specified component, fund a venture project, or hire a particular person. Potential applications in artificial intelligence, particularly in the area of pattern recognition, have further underscored the importance of the field. A recent innovation in discriminant analysis is provided by special linear programming (LP) models, which offer attractive alternatives to classical statistical approaches. The scope of application in which discriminant analysis can be advantageously employed is broadened by the flexibility to tailor parameters in the LP approaches to reflect diverse goals and by the power to explore the sensitivity of these parameters. In spite of the promise of the LP formulations, however, limitations to their effectiveness have been uncovered in certain settings. A recent advance involving a normalization construct removes some of the limitations but entails solving the LP model twice (to allow for different signs of a normalization constant) and does not yield equivalent solutions for different rotations of the problem data. This paper introduces a new model and a new class of normalizations that remedy both remaining limitations, making it possible to take advantage of the modeling capabilities of the LP formulations without the attendant shortcomings encountered by earlier investigations. Our development shows by empirical testing and illustrative analysis that the quality of solutions from LP discriminant approaches is more favorable (relative to the classical model) than previously supposed.     

A Variable Selection Criterion in the Linear Programming Approaches to Discriminant Analysis*

There are numerous variable selection rules in classical discriminant analysis. These rules enable a researcher to distinguish significant variables from nonsignificant ones and thus provide a parsimonious classification model based solely on significant variables. Prominent among such rules are the forward and backward stepwise variable selection criteria employed in statistical software packages such as Statistical Package for the Social Sciences and BMDP Statistical Software. No such criterion currently exists for linear programming (LP) approaches to discriminant analysis. In this paper, a criterion is developed to distinguish significant from nonsignificant variables for use in LP models. This criterion is based on the “jackknife” methodology. Examples are presented to illustrate implementation of the proposed criterion.     

Modification of GEE1 and linear mixed-effects models for heteroscedastic longitudinal Gaussian data

A characterization of GLMs is given. Modification of the Gaussian GEE1, modified GEE1, was applied to heteroscedastic longitudinal data, to which linear mixed-effects models are usually applied. The modified GEE1 models scale multivariate data to homoscedastic data maintaining the correlation structure and apply usual GEE1 to homoscedastic data, which needs no-diagnostics for diagonal variances. Relationships among multivariate linear regression methods, ordinary/generalized LS, naïve/modified GEE1, and linear mixed-effects models were discussed. An application showed modified GEE1 gave most efficient parameter estimation. Correct specification of the main diagonals of heteroscedastic data variance appears to be more important for efficient mean parameter estimation.