360度评估体系中权重调整方法研究

在360度评估体系中,评估者主要包括上级、下级、同级和员工本人。评估成员中往往有高估自己而低估他人的倾向,有时也会出现策略性的评估,这会导致评价结果出现偏差。另外在企业的实际应用中,各类评估者的权重往往是固定的,因此对于出现的各类偏差很难调整。本文通过分析成员的评分结果,确定成员自评与他评的一致性类型,给出了自评与他评的权重确定方法,在此基础上设计了成员的得分函数。本文的权重调整方法实际上是一种机制设计,主要是约束各类评估者策略性的评估或者降低由于心理因素带来的各类偏差。最后,将该方法应用于企业员工贡献值的排序问题,并通过与传统方法的比较,说明了此方法的有效性与合理性。     

Selecting the Correct Number of Factors in Approximate Factor Models: The Large Panel Case With Group Bridge Estimators

This article proposes a group bridge estimator to select the correct number of factors in approximate factor models. It contributes to the literature on shrinkage estimation and factor models by extending the conventional bridge estimator from a single equation to a large panel context. The proposed estimator can consistently estimate the factor loadings of relevant factors and shrink the loadings of irrelevant factors to zero with a probability approaching one. Hence, it provides a consistent estimate for the number of factors. We also propose an algorithm for the new estimator; Monte Carlo experiments show that our algorithm converges reasonably fast and that our estimator has very good performance in small samples. An empirical example is also presented based on a commonly used U.S. macroeconomic dataset.     

英语中的照应关系及其语法、语义和语用基础

对英语照应关系诸多形式的语法、语义和语用特征进行了探讨, 阐述了英语语篇照应关系之间存在的条件与约束,认为英语照应关系能够存在并使交际双方顺利进行信息传递,不仅需要以语法的一致性和语义上的重复性和限定性为基础,在某些语境条件下还需要人们忽略传统的语法一致性原则,转而借助世界知识等语用方面的线索进行语义和语境联想,进而完成整个语义链的衔接,实现概念的整合.     

On A strongly consistent nonparametric density estimator for the deconvolution problem

The problem of nonparametric estimation of a probability density function is studied when the sample observations are contaminated with random noise. Previous authors have proposed estimators which use kernel density and deconvolution techniques. The appearance and properties of the previously proposed estimators are affected by constants M_n and h_n which the user may choose. However, the optimal choices of these constants depend on the sample size n, the noise distribution and the unknown distribution which is being estimated. Hence, in practice, M_n and h_n are optimally selected as functions of the data. In this paper it is shown that a class of the proposed estimators are uniformly, strongly consistent when M_n and h_n are allowed to be random variables. Even when M_n and h_n are constants, these results are new findings.     

A test for exponentiatity against hnbue alternatives

Neyman's asymptotically most powerful test in the class of al1 similar tests for testing exponentiality against a parametric class of Makeham alternatives is shown to be consistent for the much wider class of harmonic new better than used in expectation distributions.     

Two stage fixed width confidence intervals for the common location parameter of several exponential distributins

Two stage sampling schemes are introduced for use in estimating the common location parameter (guarantee time) of two or more exponential distributions with a confidence interval of prespecified width whose coverage probability is at least a given nominal value. Exact expressions for all moments of order r ≥ 1 of the associated two stage sample sizes and for the actual coverage probabilities are derived. The performance of the procedures in a variety of two population, moderate fixed sample size cases is examined via numerical studies involving both exact calculations and Monte Carlo simulations. No new tables are needed to implement any of the proposed methods. A modified two stage procedure is recommended for practical use     

Rate of Strong Consistency of Maximum Quasi-Likelihood Estimator in Multivariate Generalized Linear Models

In this article, we consider a multivariate generalized linear model with adaptive design matrix and general link function. The asymptotic singularity of design matrix of adaptive design presents challenge when establishing the large sample properties of the maximum quasi-likelihood estimate (MQLE). Under some mild conditions, we obtain a strong convergence rate of the MQLE.     

Asymptotic Behaviors of the Lorenz Curve for Censored Data Under Strong Mixing

In this article, we consider a nonparametric estimator of the Lorenz curve under censored dependent model. We show that this estimator is uniformly strongly consistent for the associated Lorenz curve. Also, a strong Gaussian approximation for the associated Lorenz process are established under appropriate assumptions. A law of the iterated logarithm for the Lorenz process is also derived.