从“双百”方针到和谐文化——论中国共产党对主流文化的构建

1957年2月，毛泽东发表了《关于正确处理人民内部矛盾的问题》一文，提出了“百花齐放，百家争鸣”的文化建设方针；党的十六届五中全会正式提出了“社会主义核心价值观”的概念，十七大又对此作了更为详尽的阐述。“双百方针”是主流文化的指导方针；而“和谐文化”则是主流文化构建的阶段性成果。     

我国城市化滞后于工业化的成因分析

以往的研究对我国城市化滞后于工业化有所察觉,但对其原因分析较少。本文在对传统工业化道路的反思的基础上,分析我国城市化滞后于工业化的原因,认为主要不是城市化的滞后,而在于工业产值比重上升,没有相应带动产业结构和就业结构的变动,而城市化率与就业结构变动的相关性较强。进而提出要从我国的国情出发,走新型工业化道路,立足于解决就业,以工业化带动城市化。     

A hybrid Newton-type method for censored survival data using double weights in linear models

As an alternative to the Cox model, the rank-based estimating method for censored survival data has been studied extensively since it was proposed by Tsiatis [Tsiatis AA (1990) Ann Stat 18:354–372] among others. Due to the discontinuity feature of the estimating function, a significant amount of work in the literature has been focused on numerical issues. In this article, we consider the computational aspects of a family of doubly weighted rank-based estimating functions. This family is rich enough to include both estimating functions of Tsiatis (1990) for the randomly observed data and of Nan et al. [Nan B, Yu M, Kalbfleisch JD (2006) Biometrika (to appear)] for the case-cohort data as special examples. The latter belongs to the biased sampling problems. We show that the doubly weighted rank-based discontinuous estimating functions are monotone, a property established for the randomly observed data in the literature, when the generalized Gehan-type weights are used. Though the estimating problem can be formulated to a linear programming problem as that for the randomly observed data, due to its easily uncontrollable large scale even for a moderate sample size, we instead propose a Newton-type iterated method to search for an approximate solution of the (system of) discontinuous monotone estimating equation(s). Simulation results provide a good demonstration of the proposed method. We also apply our method to a real data example.     

社会科学的高度政治化及其影响——建设社会主义先进文化的一个重要问题

高度政治化是影响我国社会科学发展的一个重要因素,正确处理学术和政治的关系,必须实现学术与政治的相对分离,贯彻双百方针,促进哲学社会科学的健康发展,实现学术与政治的健康互动.     

Efficiency of Large Double Auctions

We consider large double auctions with private values. Values need be neither symmetric nor independent. Multiple units may be owned or desired. Participation may be stochastic. We introduce a very mild notion of “a little independence.” We prove that all nontrivial equilibria of auctions that satisfy this notion are asymptotically efficient. For any α>0, inefficiency disappears at rate 1/n^2‐α.     

Analytic connections between a double design and its original design in terms of different optimality criteria

In recent years, there has been increasing interest in the study of double designs. Various popular optimality criteria have been proposed from different principles for design construction and comparison, such as E(s²), generalized minimum aberration (GMA), minimum moment aberration (MMA), and minimum projection uniformity (MPU). In this article, these criteria are reviewed, and analytic connections between a double design and its original design in terms of these criteria are investigated. These connections are suitable for general original two-level factorial design, whether regular or non regular. In addition, these results provide strong insight into the relationship between double design and original design from different viewpoints.     

Choosing between two semi-finalists: On academic performance gap,sex category,and decision question

We use an application-files experimental design to investigate a new topic in the assessment of candidates for junior-engineering jobs. Our focus is on two semi-finalists, a man and a woman, who show clearly different but still good levels of academic record. We keep the gap between those levels constant, but vary the sex category of the better performer. We also include control conditions in which the two have similar records. Each assessor’s task was to choose either one applicant or neither, and to rate both in competence and suitability. The control-group competence ratings indicate no gender bias by either men or women; the experimental-conditions competence data are consistent with the candidates’ records as predicted, but also show women minimizing the record of the better-performing male candidate. Choice and suitability, on the other hand, reveal a preference for the female applicant across respondents and conditions, as anticipated given the more open nature of those two questions, while still reflecting the academic records. Findings and their interpretation are presented in detail.     

Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST)

We propose a locally efficient estimator for a class of semiparametric data combination problems. A leading estimand in this class is the average treatment effect on the treated (ATT). Data combination problems are related to, but distinct from, the class of missing data problems with data missing at random (of which the average treatment effect (ATE) estimand is a special case). Our estimator also possesses a double robustness property. Our procedure may be used to efficiently estimate, among other objects, the ATT, the two-sample instrumental variables model (TSIV), counterfactual distributions, poverty maps, and semiparametric difference-in-differences. In an empirical application, we use our procedure to characterize residual Black–White wage inequality after flexibly controlling for “premarket” differences in measured cognitive achievement. Supplementary materials for this article are available online.     

Orthogonal main-effect plans in row–column designs for two-level factorial experiments

Row–column designs for two-level factorial experiments are constructed to estimate all the main effects. We give the interactions for row and column blockings. Based on these blockings, independent treatment combinations are proposed to establish the whole design so that practitioners can easily apply it to their experiments. Some examples are given for illustrations. The estimation of two-factor interactions in these designs is discussed.     

Underdispersion models: Models that are “under the radar”

The Poisson distribution is a benchmark for modeling count data. Its equidispersion constraint, however, does not accurately represent real data. Most real datasets express overdispersion; hence attention in the statistics community focuses on associated issues. More examples are surfacing, however, that display underdispersion, warranting the need to highlight this phenomenon and bring more attention to those models that can better describe such data structures. This work addresses various sources of data underdispersion and surveys several distributions that can model underdispersed data, comparing their performance on applied datasets.