语义角色精细化理论下的题元准则研究

在语义角色精细化理论框架下讨论了英语中的题元角色与论元之间的关系。对题元准则存在的问题进行了分析，介绍了佩里尼的语义角色精细化理论，区分了精细化角色和概括性角色。精细化角色和概括性角色之间存在一条精细化轨道，连接下位的精细化题元关系和上位的语义角色的同时，把精细化题元关系和论元的句法功能联系起来。在任何谓词结构内，每个可见论元都必须充当特定的语义角色，每个语义角色都必须指派给一个可见论元。并对角色精细化题元关系为题元准则具体问题的消解方案进行了讨论。     

Composite Estimation for Single‐Index Models with Responses Subject to Detection Limits

We propose a semiparametric estimator for single‐index models with censored responses due to detection limits. In the presence of left censoring, the mean function cannot be identified without any parametric distributional assumptions, but the quantile function is still identifiable at upper quantile levels. To avoid parametric distributional assumption, we propose to fit censored quantile regression and combine information across quantile levels to estimate the unknown smooth link function and the index parameter. Under some regularity conditions, we show that the estimated link function achieves the non‐parametric optimal convergence rate, and the estimated index parameter is asymptotically normal. The simulation study shows that the proposed estimator is competitive with the omniscient least squares estimator based on the latent uncensored responses for data with normal errors but much more efficient for heavy‐tailed data under light and moderate censoring. The practical value of the proposed method is demonstrated through the analysis of a human immunodeficiency virus antibody data set.     

Small area estimation strategies for large population surveys: a comparison of design and model-based methods

Small area estimation (SAE) concerns with how to reliably estimate population quantities of interest when some areas or domains have very limited samples. This is an important issue in large population surveys, because the geographical areas or groups with only small samples or even no samples are often of interest to researchers and policy-makers. For example, large population health surveys, such as Behavioural Risk Factor Surveillance System and Ohio Mecaid Assessment Survey (OMAS), are regularly conducted for monitoring insurance coverage and healthcare utilization. Classic approaches usually provide accurate estimators at the state level or large geographical region level, but they fail to provide reliable estimators for many rural counties where the samples are sparse. Moreover, a systematic evaluation of the performances of the SAE methods in real-world setting is lacking in the literature. In this paper, we propose a Bayesian hierarchical model with constraints on the parameter space and show that it provides superior estimators for county-level adult uninsured rates in Ohio based on the 2012 OMAS data. Furthermore, we perform extensive simulation studies to compare our methods with a collection of common SAE strategies, including direct estimators, synthetic estimators, composite estimators, and Datta GS, Ghosh M, Steorts R, Maples J.'s [Bayesian benchmarking with applications to small area estimation. Test 2011;20(3):574–588] Bayesian hierarchical model-based estimators. To set a fair basis for comparison, we generate our simulation data with characteristics mimicking the real OMAS data, so that neither model-based nor design-based strategies use the true model specification. The estimators based on our proposed model are shown to outperform other estimators for small areas in both simulation study and real data analysis.     

论坚持“以德为先”选用干部

我们党选用干部标准的思想经历了从毛泽东提出的＂德才兼备＂到胡锦涛明确提出的＂德才兼备、以德为先＂的历史的、理论的发展演变过程。从宏观上和战略上看,坚持＂以德为先＂选用干部,在理论层面和现实需求层面上都具有重大的价值意义。要使＂以德为先＂用人标准不致于流于形式,就必须健全完善与时俱进的党员干部＂德＂之衡量标准。     

Decomposition of Variance for Spatial Cox Processes

Abstract. Spatial Cox point processes is a natural framework for quantifying the various sources of variation governing the spatial distribution of rain forest trees. We introduce a general criterion for variance decomposition for spatial Cox processes and apply it to specific Cox process models with additive or log linear random intensity functions. We moreover consider a new and flexible class of pair correlation function models given in terms of normal variance mixture covariance functions. The proposed methodology is applied to point pattern data sets of locations of tropical rain forest trees.     

政府采购与经济增长的实证研究——基于ARMA最优模型

本文采用基于ARMA模型适用于小样本的最优预测的建模方法,来分析政府采购和经济增长之间的因果关系。研究结果表明政府采购和经济增长之间存在非对称、单向因果关系,即政府采购对经济增长的影响不显著,而经济增长对政府采购具有促进作用。因此,从政府采购影响路径的角度分析认为,在后金融危机时代应该采取一些可行的措施扭转政府采购对经济增长的不显著状况。     

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.     

刑事证明标准的经济分析

提高证明标准,边际收益递减,边际成本递增。但证明标准不是越高越好,也不是越低越好,最佳证明标准应当由社会净收益最大化来决定。当证明标准的边际社会收益等于边际社会成本时,社会净收益实现了最大化,证明标准达到了最佳程度。对于不同性质的案件、刑事诉讼中不同阶段的诉讼行为来说,证明标准的边际收益是不同的,应当采取不同的证明标准。提高证明标准对于公、检、法等部门具有不同的边际收益和成本。部门收益和成本与社会收益和成本之间存在差异会导致部门行为的扭曲,损害社会利益。所以,必须采取措施促使部门和社会之间收益与成本趋于一致,从而实现社会利益的最大化。     

Checking validity of monotone domain mean estimators

Estimates of population characteristics such as domain means are often expected to follow monotonicity assumptions. Recently, a method to adaptively pool neighbouring domains was proposed, which ensures that the resulting domain mean estimates follow monotone constraints. The method leads to asymptotically valid estimation and inference, and can lead to substantial improvements in efficiency, in comparison with unconstrained domain estimators. However, assuming incorrect shape constraints may lead to biased estimators. Here, we develop the Cone Information Criterion for Survey Data as a diagnostic method to measure monotonicity departures on population domain means. We show that the criterion leads to a consistent methodology that makes an asymptotically correct decision choosing between unconstrained and constrained domain mean estimators. The Canadian Journal of Statistics 47: 315–331; 2019 © 2019 Statistical Society of Canada