A MOMENT IDENTITY

A moment identity based on the concept of weighted distributions is presented. The identity has potential applications to problems involving the manipulation of expectations of functions of random variables. Several examples, mainly concerning the evaluation of the risk of shrinkage estimators of several parameters, are given.     

现代语言学理论研究和后现代哲学

文化参量、文体参量及语用参量这些基本单位是建立“话语语法”体系不可缺少的要素。本文以后现代哲学为思想基础 ,揭示语言的不确定性和多元化特征     

Robust two parameter ridge M-estimator for linear regression

The problem of multicollinearity and outliers in the data set produce undesirable effects on the ordinary least squares estimator. Therefore, robust two parameter ridge estimation based on M-estimator (ME) is introduced to deal with multicollinearity and outliers in the y-direction. The proposed estimator outperforms ME, two parameter ridge estimator and robust ridge M-estimator according to mean square error criterion. Moreover, a numerical example and a Monte Carlo simulation experiment are presented.     

A fast algorithm for sampling from the posterior of a von Mises distribution

Motivated by molecular biology, there has been an upsurge of research activities in directional statistics in general and its Bayesian aspect in particular. The central distribution for the circular case is von Mises distribution which has two parameters (mean and concentration) akin to the univariate normal distribution. However, there has been a challenge to sample efficiently from the posterior distribution of the concentration parameter. We describe a novel, highly efficient algorithm to sample from the posterior distribution and fill this long-standing gap.     

Robust estimation for non-homogeneous data and the selection of the optimal tuning parameter: the density power divergence approach

The density power divergence (DPD) measure, defined in terms of a single parameter α, has proved to be a popular tool in the area of robust estimation [1 A. Basu, I.R. Harris, N.L. Hjort and M.C. Jones, Robust and efficient estimation by minimizing a density power divergence, Biometrika 85 (1998), pp. 549–559. doi: 10.1093/biomet/85.3.549[Crossref], [Web of Science ®] , [Google Scholar]]. Recently, Ghosh and Basu [5 A. Ghosh and A. Basu, Robust estimation for independent non-homogeneous observations using density power divergence with applications to linear regression, Electron. J. Stat. 7 (2013), pp. 2420–2456. doi: 10.1214/13-EJS847[Crossref], [Web of Science ®] , [Google Scholar]] rigorously established the asymptotic properties of the MDPDEs in case of independent non-homogeneous observations. In this paper, we present an extensive numerical study to describe the performance of the method in the case of linear regression, the most common setup under the case of non-homogeneous data. In addition, we extend the existing methods for the selection of the optimal robustness tuning parameter from the case of independent and identically distributed (i.i.d.) data to the case of non-homogeneous observations. Proper selection of the tuning parameter is critical to the appropriateness of the resulting analysis. The selection of the optimal robustness tuning parameter is explored in the context of the linear regression problem with an extensive numerical study involving real and simulated data.     

An Efficient Method of Parameter and Quantile Estimation for the Three-Parameter Weibull Distribution Based on Statistics Invariant to Unknown Location Parameter

The three-parameter Weibull distribution is widely used in life testing and reliability analysis. In this article, we propose an efficient method for the estimation of parameters and quantiles of the three-parameter Weibull distribution, which avoids the problem of unbounded likelihood, by using statistics invariant to unknown location. Through a Monte Carlo simulation study, we show that the proposed method performs well compared to other prominent methods based on bias and MSE. Finally, we present two illustrative examples.     

On the Strong Consistency of M‐Estimates in Linear Models for Negatively Superadditive Dependent Errors

In this paper we discuss the strong consistency of M‐estimates of the regression parameters in a linear model with negatively superadditive dependent (NSD) random errors. The result improves the moment condition and generalises the case of independent random errors to that of NSD random errors.     

小样本条件下操作风险度量中的参数估计研究

针对操作风险损失数据匮乏的特点,引入贝叶斯推断理论来保证小样本条件下模型参数估计的质量,将专家经验以先验分布的形式引入,从贝叶斯推断的视角构建了融入专家经验的操作风险损失频率模型、损失强度模型和风险度量模型。其中,损失频率建模中假设损失频率服从Poisson-Gamma分布,损失强度建模中应用极值理论对损失分布的“厚尾”特性进行描述,利用广义帕累托分布(GPD)对损失强度分布进行拟合,并打破GPD的位置参数和形状参数常数化的限制,视其为随机变量并假定两者服从不同的Gamma分布。实证结果证明: 与传统的MLE方法相比,在小样本的情况下,基于贝叶斯推断的MCMC方法对参数的估计更有效和稳定,较好地解决了在操作风险度量中由于损失数据不足给损失分布拟合带来的难题。     

重庆保税港区港口物流与区域经济协同发展研究

以系统协同理论为基础,以2008—2013年重庆保税港区港口物流和区域经济的相关数据为依托,构建协同度模型研究了两系统协同发展的情况。研究表明：重庆保税港区港口物流与区域经济是协同发展的,但协同发展程度整体偏低,重庆保税港区对区域经济的带动作用不强。据此,重庆保税港区应采取相关措施进一步拓展港口物流功能,促进两系统协同发展。     

新型无传感器感应电动机直接转矩控制系统

针对电机安装机械传感器不便的问题提出了新的速度辨识方法.从自适应控制原理和扩展卡尔曼滤波算法原理入手,将这两种不同的观测器应用于异步电机直接转矩控制,并对其进行研究和比较,找出各自的优缺点,利用Matlab/Simulink构建了系统的模型并进行仿真研究.结果表明：在负载变化、噪声抑制等方面EKF优于MARS;在状态观测方面,采用MARS优于EKF.