Missing Values in Linear Regression: Imputations Using An Error-Contaminated Linear Predictor

The problem of missing values problem is common in all branches of statistics and especially in regression analysis. Here we consider estimation of the regression parameters in the presence of missingness in the response. The usual method is to replace the missing value by its predicted value based on the available observations without any correction for the disturbance term. Instead we suggest a method which corrects the usual predictor with a guess of the disturbance term based on the available residuals. Comparison between the two methods shows that the latter leads to better results.     

Borrowing strength from past data in small domain prediction by kalman filtering - a case

Point and interval estimators for small domains based exclusively on current and domain specific sample observations are generally ineffective because of inadequate sample-sizes. So, borrowing strength from sample values for analogous domains and simultaneously from all relevant past and auxiliary data is useful in deriving improved small domain statistics. Postulating for simplicity a linear regression model with a single covariate and a zero intercept but a time-specific domain-invariant slope we start with “synthetic” generalized regression predictors for the domain totals. These borrow across only domains. For further improvements a simple autoregressive model is postulated for the slope parameters. Employing Kalman filtering the previous predictors are revised to borrow supplementary strength across time. As drastic simplifying assumptions are needed in such predictions the efficacy of the procedure is examined through an empirical exercise using live data as well as simulations. The numerical findings turn out encouraging.     

递归预测器神经网络在中国金融市场的实证研究

金融时间序列预测是金融理论领域的研究热点之一。以金融市场中普遍存在的弱混沌为基础,对递归预测器神经网络在中国金融市场的预测应用进行实证研究。在网络训练上,提出用遗传算法优化网络的阈值、权值以及激发函数的幅值和斜率。对国内股票、期货和黄金市场中几个有代表性的品种进行实证检验,计算了预测均方根误差（RMSE）和预测精度（PA）,并和两种典型的神经网络预测模型——BP神经网络、径向基函数神经网络做了比较,结果表明该模型有较好的预测效果。     

国民性理论的形象学反思--从明恩溥到鲁迅

从形象学角度分析中国现代文化史上的国民性理论中固有的矛盾性,描述自明恩溥至鲁迅的国民性理论发展过程中的形象学内涵的演变,并以此对"国民性神话论"提出质疑.     

A finite population quantile estimation by unequal probability sampling

Abstract

Using a model-assisted approach, this paper studies asymptotically design-unbiased (ADU) estimation of a population “distribution function” and extends to deriving an asymptotic and approximate unbiased estimator for a population quantile from a sample chosen with varying probabilities. The respective asymptotic standard errors and confidence intervals are then worked out. Numerical findings based on an actual data support the theory with efficient results.     

Small area estimation of proportions in business surveys

Binary data are often of interest in business surveys, particularly when the aim is to characterize grouping in the businesses making up the survey population. When small area estimates are required for such binary data, use of standard estimation methods based on linear mixed models (LMMs) becomes problematic. We explore two model-based techniques of small area estimation for small area proportions, the empirical best predictor (EBP) under a generalized linear mixed model and the model-based direct estimator (MBDE) under a population-level LMM. Our empirical results show that both the MBDE and the EBP perform well. The EBP is a computationally intensive method, whereas the MBDE is easy to implement. In case of model misspecification, the MBDE also appears to be more robust. The mean-squared error (MSE) estimation of MBDE is simple and straightforward, which is in contrast to the complicated MSE estimation for the EBP.     

Prediction for future failures in Weibull distribution under hybrid censoring

In this paper, we consider the prediction of a future observation based on a type-I hybrid censored sample when the lifetime distribution of experimental units is assumed to be a Weibull random variable. Different classical and Bayesian point predictors are obtained. Bayesian predictors are obtained using squared error and linear-exponential loss functions. We also provide a simulation consistent method for computing Bayesian prediction intervals. Monte Carlo simulations are performed to compare the performances of the different methods, and one data analysis has been presented for illustrative purposes.     

论亚当·斯密的正义思想

亚当·斯密的正义思想是其理论的重要组成部分,研究亚当·斯密的正义思想对市场经济体制下如何实现社会正义具有重要的借鉴意义。亚当·斯密的正义思想主要有:制度是正义的保障;效率是正义的标准;自由是正义的核心;同情是正义的矫正。本文对亚当·斯密的正义思想的现代意义与价值进行了评析。     

亚当·斯密的社会福利思想研究

亚当·斯密的社会福利思想,建立在整体幸福观的基础之上。这种整体幸福观,在政治上主张平等与正义,在经济上主张“富国裕民”,在伦理道德上主张整体幸福高于个人幸福。对具体福利问题,他反对限制劳动力流动的社会救济法令,主张制定符合人道的最低工资标准,进行职业保护,并提倡实施面向大众的基础教育等。这种社会福利思想是斯密伦理学与经济学思想的衍生物,也集中体现了工业化萌芽阶段英国政治、经济和社会发展的特征与要求。