Moderate Deviations for Bayes Posteriors

Let ( X_k ) k be a sequence of i.i.d. random variables taking values in a set , and consider the problem of estimating the law of X₁ in a Bayesian framework. We prove, under mild conditions on the prior, that the sequence of posterior distributions satisfies a moderate deviation principle.     

A simple procedure for the selection of significant effects

Summary.  Given a large number of test statistics, a small proportion of which represent departures from the relevant null hypothesis, a simple rule is given for choosing those statistics that are indicative of departure. It is based on fitting by moments a mixture model to the set of test statistics and then deriving an estimated likelihood ratio. Simulation suggests that the procedure has good properties when the departure from an overall null hypothesis is not too small.     

Seemingly unrelated regression on the autoregressive (Ar(p)) singular equation system

This paper studies the estimation of seemingly unrelated regressions (SUR) of singular equation systems with an autoregressive error process (AR(p)) for each equation.Parameter estimates of the autoregressive singular equation system are not generally invariant to the equation deleted. Under the model specification restriction on the autoregressive parameters, the invariance property is preserved, and this paper shows that a single equation generalized least squares (GLS) estimation for a general autoregressive error process is equivalent to the SURGLS estimation of the AR(p) singular equation system.     

近现代中国人观念现代化的历史进程与启示

近现代中国,观念现代化经历了鸦片战争以后的三次思想解放和启蒙阶段,五四运动以后马克思主义广泛传播和新民主主义革命时期的深化阶段。它给我们留下了深刻的启示。     

基于BPR的组织学习

BPR是组织在适应快速变化环境中最常用且最具革命性的方法,它能很好地促进组织进行创新性学习。本文旨在聚焦于BPR活动中的项目组和指导委员会来研究BPR在项目初始、分析和再设计阶段中通过流程定位和确定顾客偏好来促进组织学习问题,从而提出并建立一种组织学习的BPR方法促进论。     

Innovation through initiatives—a framework for building new capabilities in public sector research organizations

The accelerating pace of change in science and technology has resulted in new attention to the process of identifying and developing ideas that ultimately lead to new scientific capabilities and business opportunities for an organization. The need to refresh research programs and capabilities is as important in federally funded research institutions as it is for industry. This paper explores the critical success factors for new initiatives at a federal laboratory, and building on lessons learned through this study and in private industry, identifies a more systematic process that could potentially improve the effectiveness of these initiatives in achieving results.     

人力资源个体价值计量方法——当期实现价值系数法

由于人力资源自身具有复杂性、不确定性的特点,使得人力资源价值难以计量。而准确的计量人力资源价值,尤其是人力资源的个体价值是人力资源会计能否实施的关键因素。针对这个问题,本文在分析前人成果的基础上提出了人力资源当期实现价值系数法计量模型。并通过实例验证了计量模型的实用性。     

Computation of an efficient and robust estimator in a semiparametric mixture model

In this article, we propose an efficient and robust estimation for the semiparametric mixture model that is a mixture of unknown location-shifted symmetric distributions. Our estimation is derived by minimizing the profile Hellinger distance (MPHD) between the model and a nonparametric density estimate. We propose a simple and efficient algorithm to find the proposed MPHD estimation. Monte Carlo simulation study is conducted to examine the finite sample performance of the proposed procedure and to compare it with other existing methods. Based on our empirical studies, the newly proposed procedure works very competitively compared to the existing methods for normal component cases and much better for non-normal component cases. More importantly, the proposed procedure is robust when the data are contaminated with outlying observations. A real data application is also provided to illustrate the proposed estimation procedure.     

Nonparametric Bayesian inference for multivariate density functions using Feller priors

Multivariate density estimation plays an important role in investigating the mechanism of high-dimensional data. This article describes a nonparametric Bayesian approach to the estimation of multivariate densities. A general procedure is proposed for constructing Feller priors for multivariate densities and their theoretical properties as nonparametric priors are established. A blocked Gibbs sampling algorithm is devised to sample from the posterior of the multivariate density. A simulation study is conducted to evaluate the performance of the procedure.     

Construction of closed interval for process capability indices Cpu,Cpl, and Spk based on Boole’s inequality and de Morgan’s laws

ABSTRACT

Considerable effort has been spent on the development of confidence intervals for process capability indices (PCIs) based on the sampling distribution of the PCI or the transferred PCI. However, there is still no definitive way to construct a closed interval for a PCI. The aim of this study is to develop closed intervals for the PCIs C_pu, C_pl, and S_pk based on Boole's inequality and de Morgan's laws. The relationships between different sample sizes, the significance levels, and the confidence intervals of the PCIs C_pu, C_pl, and S_pk are investigated. Then, a testing model for interval estimation for the PCIs C_pu, C_pl, and S_pk is built as a powerful tool for measuring the quality performance of a product. Finally, an applied example is given to demonstrate the effectiveness and applicability of the proposed method and the testing model.