Low dose risk estimation via simultaneous statistical inferences

Summary.  The paper develops and studies simultaneous confidence bounds that are useful for making low dose inferences in quantitative risk analysis. Application is intended for risk assessment studies where human, animal or ecological data are used to set safe low dose levels of a toxic agent, but where study information is limited to high dose levels of the agent. Methods are derived for estimating simultaneous, one-sided, upper confidence limits on risk for end points measured on a continuous scale. From the simultaneous confidence bounds, lower confidence limits on the dose that is associated with a particular risk (often referred to as a bench-mark dose ) are calculated. An important feature of the simultaneous construction is that any inferences that are based on inverting the simultaneous confidence bounds apply automatically to inverse bounds on the bench-mark dose.     

A bayesian estimator for the dependence function of a bivariate extreme‐value distribution

Any continuous bivariate distribution can be expressed in terms of its margins and a unique copula. In the case of extreme‐value distributions, the copula is characterized by a dependence function while each margin depends on three parameters. The authors propose a Bayesian approach for the simultaneous estimation of the dependence function and the parameters defining the margins. They describe a nonparametric model for the dependence function and a reversible jump Markov chain Monte Carlo algorithm for the computation of the Bayesian estimator. They show through simulations that their estimator has a smaller mean integrated squared error than classical nonparametric estimators, especially in small samples. They illustrate their approach on a hydrological data set.     

基于局部社团结构平衡的双模符号网络链路预测研究

随着互联网产业的高速发展,双模符号网络已经成为一类常见的复杂网络,然而针对此 类网络的分析较少。本文在传统非符号网络局部社团理论和符号网络结构平衡理论的基础上,首次提出了双模符号网络下的局部社团理论。这一理论不仅考虑了符号网络中共同邻居的信息,还引入了共同邻居间存在的连接。进一步地,本文推导出符号网络中基于局部社团信息的加权平衡回路增益指数,该指标可以表示双模符号网络中用户节点和产品节点间的符号关系。为了将该指标更好地应用于双模 符号网络链路预测问题,本文提出了加权平衡回路增益分类器算法。实验结果表明,相比其他经典链路预测算法,新算法具有更好的预测能力。     

Coversge probabilty to the original data by prediction intervals in simple linear regression

The coverage rate of the original data by the prediction interval in simple linear regression is obtained by computer simulation. The results show that for small sample size, the coverage rate is higher than the assigned prediction coverage rate (confidence level). The two coverage rates begin to converge when the sample size is larger than 50 and the convergence rate depends very little on the distribution of the independent variable. Also, theoretical results on the asymptotic coverage rate and on the absolute minimum bounds are obtained     

金融市场收益率方向预测模型研究———基于文本大数据方法

金融市场的发展关系着一国的经济命脉,而股票市场作为金融市场的重要组成部分,对其收益率的研究也一直都是学术界的热点。财经新闻常被认为蕴含着丰富的信息,其中所包含的情感信息作为影响投资者投资决策的重要因素之一,对股票收益率也具有一定的影响。故本文构建了适用于金融投资领域的财经新闻情感词典来对财经新闻进行文本分析,同时构造了新的预测模型:将财经新闻文本中所含的情感量化为情绪指数并与时变密度函数相结合,得到时变加权密度模型。并在此基础上以模型评分为权重组合多个预测模型构建出评分加权模型用于股票收益率预测。结果显示,加入情绪指数能有效提高模型预测能力,而评分加权模型的预测能力则在此基础上更进一步,在准确率以及评分规则上基本达到双重最优。     

岩石可钻性能量法现场实验研究

本文介绍了岩石可钻性能全法在油田现场实脸研究的成果:确定其可钻性指标凡对地层的适用范围，证明K指标比其他可钻性指标能更容易、史好地反映出井下实际地层可钻性变化情无，用K指标预浏钻速与实际钻速接近.     

Prediction intervals for farima processes by bootstrap methods

In this paper we introduce a procedure to compute prediction intervals for FARIMA (p d q) processes, taking into account the variability due to model identification and parameter estimation. To this aim, a particular bootstrap technique is developed. The performance of the prediction intervals is then assessed and compared to that of stand­ard bootstrap percentile intervals. The methods are applied to the time series of Nile River annual minima.     

论现代经济预测方法的分类原理

分析了现代经济预测的定化、面对数据流、灰色、非线性、动态和智能化等主要特征。简要回顾了经济预测的传统分类方法，认为它己不能很好地满足现代经济预测的要求。然后，根据现代经济的预测特征，结合现代数学、计算机技术和信息技术等的发展，提出了一种新的经济预测分类方法。并阐述了这种分类方法在现代经济预测中所适用的范围。由于现代经济的变化是复杂多变的，在预测时为了准确、真买、迅速地反映其运行规律，有时采用多种预测方法组合预测，有时采用动态仿真预测。     

Prediction of future failures times based on Type-I hybrid censored samples of random sample sizes

In this article, we consider the prediction of future failure times based on Type-I hybrid censored samples. Point predictors and prediction intervals using different procedures are discussed for a general model. The exponential and Rayleigh distributions are used as illustrative examples to show the most simplified forms of the so obtained predictors as well as prediction intervals. Intensive simulation study and a real life dataset are presented to illustrate our findings and results.     

Forecasting obesity trends in England

Summary.  Forecasts of trends in obesity in England for 2010 are produced by treating the available data, which contain the proportions of the population, categorized by age and sex, falling into different body mass index ranges, as compositional data sets, so that the implicit simplex restrictions are automatically satisfied. Forecasts are calculated by using linear trend models for the log-ratio transformations and are accompanied by prediction regions. The advantages of treating data on proportions compositionally are emphasized and compared with forecasts that have been obtained by ignoring this restriction.