汉语不平等同义并列式双音词研究

目前,语法学界对汉语同义并列式双音词构词法的分析只到"并列"为止,没有进一步深入探讨这两个语素是怎样的并列状态。笔者认为同义并列式双音词的两个语素地位并不是绝对平等的,据此,可以把不平等同义并列式双音词分为三种类型———后限定式、前限定式、附加式。前、后限定式的不平等同义并列分别与偏正、动补结构在表意上具有相同的作用,这种跨结构的表意上的一致性,归根到底是为了适应汉语双音化和表意精确化的要求。     

分数阶长短波方程的整体光滑解

本文研究了分数阶长短波方程组的周期初边值问题,运用一致先验估计和Galerkin方法证明了分数阶长短波方程整体光滑解的存在性和唯一性.     

Need,well‐being and outcomes: the development of policy‐thinking for children's services 1989–2004

This paper describes how government policy thinking about the well‐being of children and young people developed between the Children Act 1989 and the Children Act 2004. These two Acts are milestone statements about how services to children in England and Wales should be delivered. It is an account informed by the author's own experience as a government adviser on children's social care over much of this period and supporting documentation. It traces the strands of government policy thinking about how to deliver services for children from children in need to the articulation of the five Every Child Matters outcomes. It argues that attempts to achieve coordinated service planning for children and young people played a significant role in the formulation of shared objectives and the articulation of child outcomes. However, it argues that looking at real outcomes exposes how children in the UK do relatively badly compared with other rich nations. It questions whether we can realistically expect our services to deliver significantly improved outcomes given the impact of enduring inequality in our society. This paper is dedicated to the memory of David Lambert CBE, former Assistant Chief Inspector of the Social Services Inspectorate in London, who died suddenly on 7 October 2010. He lent me articles to assist me in writing this paper.     

Identifying Latent Structures in Panel Data

This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown. Two approaches are considered—penalized profile likelihood (PPL) estimation for the general nonlinear models without endogenous regressors, and penalized GMM (PGMM) estimation for linear models with endogeneity. In both cases, we develop a new variant of Lasso called classifier‐Lasso (C‐Lasso) that serves to shrink individual coefficients to the unknown group‐specific coefficients. C‐Lasso achieves simultaneous classification and consistent estimation in a single step and the classification exhibits the desirable property of uniform consistency. For PPL estimation, C‐Lasso also achieves the oracle property so that group‐specific parameter estimators are asymptotically equivalent to infeasible estimators that use individual group identity information. For PGMM estimation, the oracle property of C‐Lasso is preserved in some special cases. Simulations demonstrate good finite‐sample performance of the approach in both classification and estimation. Empirical applications to both linear and nonlinear models are presented.     

Health Risk,Inequality Indexes,and Environmental Justice

Inequality indexes have long been used to analyze distributions of income. Studies have recently begun to use these tools to evaluate the equity of distributions of environmental harm. In response, issues have been raised regarding the appropriateness of using income-based measures in the context of undesirable outcomes. We begin from first principles, identifying a theoretical preference structure under which income-based tools can be appropriate for ranking distributions of “bads.” While some critiques of existing applications are valid, they are not a justification for rejecting the approach altogether. Instead, we show how standard income-based measures can be adjusted to accommodate bad outcomes. Rather than inequality indexes, we argue that equally distributed equivalents (EDEs) are well-suited for this purpose since they account for levels and dispersion of outcome distributions. The Kolm–Pollak EDE is particularly useful, having the advantage of consistently evaluating both bads and their complementary goods (e.g., mortality risk and survival probability). As an illustration, we show how these tools can inform an environmental justice analysis of a proposed Environmental Protection Agency (EPA) rule addressing indoor air pollution.     

如何从生命周期理解我国的收入不平等

文章从生命周期的角度入手,以期为理解收入不平等提供一个不同的视角.基尼系数本质上衡量的是结果的不平等,收入所得税等政府政策也侧重对结果不平等的调整,但收入结果的不平等起源于机会的不平等,强化于过程的不平等,最终才表现为结果的不平等.机会不平等、过程不平等和结果不平等对应不同的生命周期阶段,需要不同的政策措施.对于机会不平等,政府应着眼于消除户籍制度等对不同人群的制度性歧视,减少我国区域发展水平的差距;推动幼儿和青少年教育与医疗服务的均等化,为他们创造一个公平的养教环境.对于过程不平等,一个重要的方面是建立对各种所有制员工和不同雇佣关系职工都公平的城乡统一的市场环境,消除劳动力市场中同工不同酬等歧视现象,尤其是取消国家制度和政策造成的劳动力市场分割.对于结果不平等,要兼顾公平和效率,在现有个人所得税、最低生活保障制度等基础上,逐步建立城乡统一、公平公正的收入调节和社会保障体系.     

A Necessary Condition for the Strong Oracle Property

A problem of using a non‐convex penalty for sparse regression is that there are multiple local minima of the penalized sum of squared residuals, and it is not known which one is a good estimator. The aim of this paper is to give a guide to design a non‐convex penalty that has the strong oracle property. Here, the strong oracle property means that the oracle estimator is the unique local minimum of the objective function. We summarize three definitions of the oracle property – the global, weak and strong oracle properties. Then, we give sufficient conditions for the weak oracle property, which means that the oracle estimator becomes a local minimum. We give an example of non‐convex penalties that possess the weak oracle property but not the strong oracle property. Finally, we give a necessary condition for the strong oracle property.     

Oracle Inequalities for Convex Loss Functions with Nonlinear Targets

This article considers penalized empirical loss minimization of convex loss functions with unknown target functions. Using the elastic net penalty, of which the Least Absolute Shrinkage and Selection Operator (Lasso) is a special case, we establish a finite sample oracle inequality which bounds the loss of our estimator from above with high probability. If the unknown target is linear, this inequality also provides an upper bound of the estimation error of the estimated parameter vector. Next, we use the non-asymptotic results to show that the excess loss of our estimator is asymptotically of the same order as that of the oracle. If the target is linear, we give sufficient conditions for consistency of the estimated parameter vector. We briefly discuss how a thresholded version of our estimator can be used to perform consistent variable selection. We give two examples of loss functions covered by our framework.     

Variable selection in additive quantile regression using nonconcave penalty

This paper considers variable selection in additive quantile regression based on group smoothly clipped absolute deviation (gSCAD) penalty. Although shrinkage variable selection in additive models with least-squares loss has been well studied, quantile regression is sufficiently different from mean regression to deserve a separate treatment. It is shown that the gSCAD estimator can correctly identify the significant components and at the same time maintain the usual convergence rates in estimation. Simulation studies are used to illustrate our method.