构建和谐劳动关系的困境与对策

建立和谐稳定的劳动关系是构建社会主义和谐社会的重要基础和基本内容。本文探究了当前我国劳动关系主体多元化、复杂化,劳动关系的调整机制不健全,劳动关系出现诸多不和谐等状况,分析了制约和谐劳动关系建立的多方面因素,积极探寻构建和谐劳动关系的途径,促进和谐劳动关系的形成。     

论新时代农村基层矛盾化解的共治能力建设

新时代的农村基层矛盾由传统民间私人领域向市场领域和公共领域拓展，呈现出多样化、复杂化、易激化等阶段性特征，化解难度明显加大。农村基层矛盾化解的机制创新，突出体现在由各负其责的分治向多主体协同联动的共治转型，力求破解单主体分而治之的碎片化困境，但多主体间的共治能力建设滞后已逐渐成为农村基层矛盾化解的一大瓶颈。全方位、系统化的能力提升则是应对农村基层矛盾化解共治能力建设滞后的因应方略，旨在通过“自解-他解-共解”并举、“共商-协同-联动”并进、“自治-法治-德治”融合的能力建设来弥补共治能力建设滞后的不足。最终聚合治理资源，凝聚治理合力，协同共促乡村社会和谐稳定发展。     

A new reproducing kernel-based nonlinear dimension reduction method for survival data

Based on the theories of sliced inverse regression (SIR) and reproducing kernel Hilbert space (RKHS), a new approach RDSIR (RKHS-based Double SIR) to nonlinear dimension reduction for survival data is proposed. An isometric isomorphism is constructed based on the RKHS property, then the nonlinear function in the RKHS can be represented by the inner product of two elements that reside in the isomorphic feature space. Due to the censorship of survival data, double slicing is used to estimate the weight function to adjust for the censoring bias. The nonlinear sufficient dimension reduction (SDR) subspace is estimated by a generalized eigen-decomposition problem. The asymptotic property of the estimator is established based on the perturbation theory. Finally, the performance of RDSIR is illustrated on simulated and real data. The numerical results show that RDSIR is comparable with the linear SDR method. Most importantly, RDSIR can also effectively extract nonlinearity from survival data.     

Estimation of conditional cumulative incidence functions under generalized semiparametric regression models with missing covariates,with application to analysis of biomarker correlates in vaccine trials

This article presents generalized semiparametric regression models for conditional cumulative incidence functions with competing risks data when covariates are missing by sampling design or happenstance. A doubly robust augmented inverse probability weighted (AIPW) complete-case approach to estimation and inference is investigated. This approach modifies IPW complete-case estimating equations by exploiting the key features in the relationship between the missing covariates and the phase-one data to improve efficiency. An iterative numerical procedure is derived to solve the nonlinear estimating equations. The asymptotic properties of the proposed estimators are established. A simulation study examining the finite-sample performances of the proposed estimators shows that the AIPW estimators are more efficient than the IPW estimators. The developed method is applied to the RV144 HIV-1 vaccine efficacy trial to investigate vaccine-induced IgG binding antibodies to HIV-1 as correlates of acquisition of HIV-1 infection while taking account of whether the HIV-1 sequences are near or far from the HIV-1 sequences represented in the vaccine construct.     

Preference for Flexibility in a Savage Framework

We study preferences over Savage acts that map states to opportunity sets and satisfy the Savage axioms. Preferences over opportunity sets may exhibit a preference for flexibility due to an implicit uncertainty about future preferences reflecting anticipated unforeseen contingencies. The main result of this paper characterizes maximization of the expected indirect utility in terms of an ‘Indirect Stochastic Dominance’ axiom that expresses a preference for ‘more opportunities in expectation.’ The key technical tool of the paper, a version of Möbius inversion, has been imported from the theory of nonadditive belief functions; it allows an alternative representation using Choquet integration, and yields a simple proof of Kreps' (1979) classic result.