关于创新行政层级监督新机制的思考

行政层级监督虽然是行政系统内固有的、原初的监督形式，但并不能证明其自身就能运作良好。目前，我国行政层级监督明显无法应对政令不通和“政府弱、部门强”的问题，主要原因在于行政层级监督中干预过度与监督虚置并存，方法缺乏创新，过分重视事前审批和事后监督，行政层级监督有内部化和神秘化倾向。强化行政层次监督必须在明确以下两个问题的基础上：行政层级监督的基本目标——纠错、发现原因并督促完善机制的建立；行政层级监督有效运作的基本前提——被监督机关能相对独立行使职权和上下级之间的职权职责明晰。要区分不同层级监督的范围和重点，完善重大案件案卷定期评察制度，增加监督的透明度，加强层次监督的规范化并强化监督者的责任。     

科学发展观与高校学习型干部队伍建设

针对高校干部队伍的发展问题,结合新时期学习型干部队伍建设的特点,采用文献法,理论分析法,假设法等方法,运用教育学、政治学、社会管理学与领导科学等多学科知识,对高等教育学理论和管理方法进行研究,完善了高校管理队伍的职业化建设等途径的研究,制定了高校学习型干部队伍监督管理体制。     

Local rank estimation and related test for varying-coefficient partially linear models

This paper develops a robust estimation procedure for the varying-coefficient partially linear model via local rank technique. The new procedure provides a highly efficient and robust alternative to the local linear least-squares method. In other words, the proposed method is highly efficient across a wide class of non-normal error distributions and it only loses a small amount of efficiency for normal error. Moreover, a test for the hypothesis of constancy for the nonparametric component is proposed. The test statistic is simple and thus the test procedure can be easily implemented. We conduct Monte Carlo simulation to examine the finite sample performance of the proposed procedures and apply them to analyse the environment data set. Both the theoretical and the numerical results demonstrate that the performance of our approach is at least comparable to those existing competitors.     

A WEIGHTED RANK MEASURE OF CORRELATION

Spearman's rank correlation coefficient is not entirely suitable for measuring the correlation between two rankings in some applications because it treats all ranks equally. In 2000, Blest proposed an alternative measure of correlation that gives more importance to higher ranks but has some drawbacks. This paper proposes a weighted rank measure of correlation that weights the distance between two ranks using a linear function of those ranks, giving more importance to higher ranks than lower ones. It analyses its distribution and provides a table of critical values to test whether a given value of the coefficient is significantly different from zero. The paper also summarizes a number of applications for which the new measure is more suitable than Spearman's.     

Nest代数的环自同构

给出了Ｂａｎａｃｈ空间中Ｎｅｓｔ代数ＡｌｇＮ的环自同构的形式．     

Illustration of Sampling-Based Methods for Uncertainty and Sensitivity Analysis

A sequence of linear, monotonic, and nonmonotonic test problems is used to illustrate sampling-based uncertainty and sensitivity analysis procedures. Uncertainty results obtained with replicated random and Latin hypercube samples are compared, with the Latin hypercube samples tending to produce more stable results than the random samples. Sensitivity results obtained with the following procedures and/or measures are illustrated and compared: correlation coefficients (CCs), rank correlation coefficients (RCCs), common means (CMNs), common locations (CLs), common medians (CMDs), statistical independence (SI), standardized regression coefficients (SRCs), partial correlation coefficients (PCCs), standardized rank regression coefficients (SRRCs), partial rank correlation coefficients (PRCCs), stepwise regression analysis with raw and rank-transformed data, and examination of scatter plots. The effectiveness of a given procedure and/or measure depends on the characteristics of the individual test problems, with (1) linear measures (i.e., CCs, PCCs, SRCs) performing well on the linear test problems, (2) measures based on rank transforms (i.e., RCCs, PRCCs, SRRCs) performing well on the monotonic test problems, and (3) measures predicated on searches for nonrandom patterns (i.e., CMNs, CLs, CMDs, SI) performing well on the nonmonotonic test problems.     

COMBINING MULTIPLE MARKERS FOR MULTI‐CATEGORY CLASSIFICATION: AN ROC SURFACE APPROACH

The Receiver Operating Characteristic (ROC) curve and the Area Under the ROC Curve (AUC) are effective statistical tools for evaluating the accuracy of diagnostic tests for binary‐class medical data. However, many real‐world biomedical problems involve more than two categories. The Volume Under the ROC Surface (VUS) and Hypervolume Under the ROC Manifold (HUM) measures are extensions for the AUC under three‐class and multiple‐class models. Inference methods for such measures have been proposed recently. We develop a method of constructing a linear combination of markers for which the VUS or HUM of the combined markers is maximized. Asymptotic validity of the estimator is justified by extending the results for maximum rank correlation estimation that are well known in econometrics. A bootstrap resampling method is then applied to estimate the sampling variability. Simulations and examples are provided to demonstrate our methods.     

Testing different rank order question layouts for PC and smartphone respondents

We studied the impact of different layouts for rank order questions on respondent effort, data quality, and substantive results among PC and smartphone respondents, in an experiment in an opt-in online panel in Spain, using an order-by-click design. We experimentally varied the device, the number of columns, and, for smartphone respondents, the position of the ‘next’ button in questions on trust in institutions.

We found some evidence of lower data quality for smartphone users but no evidence that presenting ranking items in one column performs differently than two columns. We also find little evidence that these effects differ by the number of response options presented or the number to be ranked. The placement of the ‘next’ button had little effect on performance on ranking items. Overall, our findings suggest that the format and layout of order-by-click questions has little effect on data quality, regardless of device used.     

Analysis of Double Single Index Models

Motivated from problems in canonical correlation analysis, reduced rank regression and sufficient dimension reduction, we introduce a double dimension reduction model where a single index of the multivariate response is linked to the multivariate covariate through a single index of these covariates, hence the name double single index model. Because nonlinear association between two sets of multivariate variables can be arbitrarily complex and even intractable in general, we aim at seeking a principal one‐dimensional association structure where a response index is fully characterized by a single predictor index. The functional relation between the two single‐indices is left unspecified, allowing flexible exploration of any potential nonlinear association. We argue that such double single index association is meaningful and easy to interpret, and the rest of the multi‐dimensional dependence structure can be treated as nuisance in model estimation. We investigate the estimation and inference of both indices and the regression function, and derive the asymptotic properties of our procedure. We illustrate the numerical performance in finite samples and demonstrate the usefulness of the modelling and estimation procedure in a multi‐covariate multi‐response problem concerning concrete.     

Ties in one block comparison experiments: a generalization of the Mallows–Bradley–Terry ranking model

This study is concerned with the extension of the Mallows–Bradley–Terry ranking model for one block comparison consisting of all the items of interest to situations which allow an expression of no preference. We consider a modification of the Mallows–Bradley–Terry ranking model by introducing an additional parameter, called an index of discrimination, in the model. This permits ties in the model. The maximum likelihood estimates of the parameters are found using a Maximization–Minimization algorithm: the evaluation of the mathematical expectations involved in the log-likelihood equation is obtained by generating samples of Monte Carlo Markov chain from the stationary distribution. In addition, a simulation study for asymptotic properties assessment has been made. The proposed method is applied to analyze data election.