A multivariate zero-inflated binomial model for the analysis of correlated proportional data

In this paper, a new multivariate zero-inflated binomial (MZIB) distribution is proposed to analyse the correlated proportional data with excessive zeros. The distributional properties of purposed model are studied. The Fisher scoring algorithm and EM algorithm are given for the computation of estimates of parameters in the proposed MZIB model with/without covariates. The score tests and the likelihood ratio tests are derived for assessing both the zero-inflation and the equality of multiple binomial probabilities in correlated proportional data. A limited simulation study is performed to evaluate the performance of derived EM algorithms for the estimation of parameters in the model with/without covariates and to compare the nominal levels and powers of both score tests and likelihood ratio tests. The whitefly data is used to illustrate the proposed methodologies.     

农村居民消费对我国经济增长影响分析

本文通过建立面板数据模型测度了农村居民消费对我国经济增长的影响效应,认为农村居民消费对我国经济增长具有正向促进作用但不够明显,分析了扩大农村居民消费需求促进经济增长所面临的障碍因素并提出了相应的对策建议。     

Rate efficient estimation of realized Laplace transform of volatility with microstructure noise

In this paper, we consider the problem of estimating the Laplace transform of volatility within a fixed time interval [0,T] using high‐frequency sampling, where we assume that the discretized observations of the latent process are contaminated by microstructure noise. We use the pre‐averaging approach to deal with the effect of microstructure noise. Under the high‐frequency scenario, we obtain a consistent estimator whose convergence rate is , which is known as the optimal convergence rate of the estimation of integrated volatility functionals under the presence of microstructure noise. The related central limit theorem is established. The simulation studies justify the finite‐sample performance of the proposed estimator.     

能源价格波动对中国物价水平的潜在与实际影响

目的/意义原油作为重要的工业原料、大宗商品和战略储备物资,其价格波动对全球经济发展具有重大影响,对原油价格进行预测是能源经济领域的热点研究课题。对国际原油价格预测的主要方法进行系统梳理并展望未来研究方向,有助于该研究领域的纵深发展。设计/方法基于295篇国际期刊文献,对国际油价预测的发展阶段、发表期刊、研究机构等进行归纳总结,然后对近二十年油价预测领域的主要研究方法进行系统梳理和分析,最后对油价预测工作进行评述和趋势展望。结论/发现自2008年金融危机后,油价预测研究快速发展,相关文章主要发表于能源经济领域的权威期刊《Energy Economic》。现有油价预测方法主要包括计量模型、机器学习和混合预测模型。展望未来,利用高频日内交易数据,融合计量和机器学习前沿方法,对原油价格进行区间预测或概率密度预测是值得探索的方向,此外还应深化油价预测结果的实际应用价值。     

农村老年人老化态度变动轨迹:基于积极老龄化框架的实证考察

基于积极老龄化理论框架和"中国老年健康影响因素跟踪调查"面板数据,文章从积极和消极两个维度对农村老年人老化态度问题进行实证考察。研究发现,两种老化态度均以2008年为重要转折点,女性老化态度比男性更具多样性和多变性。健康、社会参与和保障都能引起老化态度的积极变动,但社会适应、子女经济支持和社区照料三个因素对消极老化态度有正向影响,而生产活动降低了积极老化态度水平。从贡献率看,健康因素对老化态度差异贡献最大,其次是保障因素、社会参与因素,健康因素更能解释积极老化态度的变化,社会参与因素对消极老化态度的贡献率更高,二者分别在女性和男性老年人群体中表现得更为明显。贡献率排在前四位的因素是精神健康、子女经济支持、医疗可及性和身体健康。用积极老龄化的观点帮助庞大的农村老年人口建立正确的老化态度将有益于养老问题的解决。     

数据挖掘中关联规则的规范化描述

一、引言数据挖掘(Data Mining)是近年来随着人工智能和数据库技术的发展而出现的一门新兴学科。它是从大量的数据中筛选出隐含的、可信的、新颖的、有效的信息的高级处理过程。关联规则(Association Rule)是其中重要的研究课题,是数据挖掘的主要技术之一,也是在无指导学习系统     

数据挖掘中属性项压缩的统计方法研究

文章试图将统计思想与(Rough)粗糙集理论相结合,针对事务性数据库属性项压缩问题提出了一些行之有效的方法,即基于重要性的属性压缩、基于相依性的属性压缩、属性项的广义线形分析及压缩、基于多重相关性的属性项压缩,以此达到数据库压缩之目的。     

高斯与观测误差分布的发现

在数理统计的发展史上,最重要的事件也许就是观测误差分布的发现。统计学家们在寻找观测误差分布的过程中,创造了许多有用的统计理论和方法,不过观测误差分布发现的优先权最终属于伟大的德国科学家高斯。文章主要介绍了高斯发现观测误差分布的思考过程,并期望读者能从高斯天才的思想中获得有益的启迪。     

Bayesian palaeoclimate reconstruction

Summary.  We consider the problem of reconstructing prehistoric climates by using fossil data that have been extracted from lake sediment cores. Such reconstructions promise to provide one of the few ways to validate modern models of climate change. A hierarchical Bayesian modelling approach is presented and its use, inversely, is demonstrated in a relatively small but statistically challenging exercise: the reconstruction of prehistoric climate at Glendalough in Ireland from fossil pollen. This computationally intensive method extends current approaches by explicitly modelling uncertainty and reconstructing entire climate histories. The statistical issues that are raised relate to the use of compositional data (pollen) with covariates (climate) which are available at many modern sites but are missing for the fossil data. The compositional data arise as mixtures and the missing covariates have a temporal structure. Novel aspects of the analysis include a spatial process model for compositional data, local modelling of lattice data, the use, as a prior, of a random walk with long-tailed increments, a two-stage implementation of the Markov chain Monte Carlo approach and a fast approximate procedure for cross-validation in inverse problems. We present some details, contrasting its reconstructions with those which have been generated by a method in use in the palaeoclimatology literature. We suggest that the method provides a basis for resolving important challenging issues in palaeoclimate research. We draw attention to several challenging statistical issues that need to be overcome.