城镇化背景下农产品物流发展模式研究

农产品物流作为物流业的一个分支,在促进农业产业化和城镇化发展方面发挥着重要作用。对原有农产品物流发展模式进行分析,构建四种农产品物流发展模式。从城镇化水平的角度出发,以辽宁省为例,运用主成分聚类分析法对辽宁省14个地区的城镇化水平进行评价及分类,并针对不同城镇化水平的地区进行农产品物流发展模式的选择,旨在为促进农产品物流的一体化发展提供借鉴。     

A Comparison of Some Methods for the Selection of a Common Eigenvector Model for the Covariance Matrices of Two Groups

Two new nonparametric common principal component model selection procedures based on bootstrap distributions of the vector correlations of all combinations of the eigenvectors from two groups are proposed. The performance of these methods is compared in a simulation study to the two parametric methods previously suggested by Flury in 1988, as well as modified versions of two nonparametric methods proposed by Klingenberg in 1996 and then by Klingenberg and McIntyre in 1998. The proposed bootstrap vector correlation distribution (BVD) method is shown to outperform all of the existing methods in most of the simulated situations considered.     

Probability Generating Function Based Jeffrey's Divergence for Statistical Inference

Statistical inference procedures based on transforms such as characteristic function and probability generating function have been examined by many researchers because they are much simpler than probability density functions. Here, a probability generating function based Jeffrey's divergence measure is proposed for parameter estimation and goodness-of-fit test. Being a member of the M-estimators, the proposed estimator is consistent. Also, the proposed goodness-of-fit test has good statistical power. The proposed divergence measure shows improved performance over existing probability generating function based measures. Real data examples are given to illustrate the proposed parameter estimation method and goodness-of-fit test.     

The k-factor GARMA Process with Infinite Variance Innovations

In this article, we develop the theory of k-factor Gegenbauer Autoregressive Moving Average (GARMA) process with infinite variance innovations which is a generalization of the stable seasonal fractional Autoregressive Integrated Moving Average (ARIMA) model introduced by Diongue et al. (2008 Diongue, A.K., Guégan, D. (2008). Estimation of k-Factor GIGARCH Process: A Monte Carlo Study. Communications in Statistics-Simulation and Computation 37:2037–2049.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). Stationarity and invertibility conditions of this new model are derived. Conditional Sum of Squares (CSS) and Markov Chains Monte Carlo (MCMC) Whittle methods are investigated for parameter estimation. Monte Carlo simulations are also used to evaluate the finite sample performance of these estimation techniques. Finally, the usefulness of the model is corroborated with the application to streamflow data for Senegal River at Bakel.     

Symmetrical Design of Experiment in Global Sensitivity Analysis Based on ANOVA High-dimensional Model Representation

This article reviews symmetrical global sensitivity analysis based on the analysis of variance of high-dimensional model representation. To overcome the computational difficulties and explore the use of symmetrical design of experiment (SDOE), two methods are presented. If the form of the objective function f is known, we use SDOE to estimate the symmetrical global sensitivity indices instead of Monte Carlo or quasi-Monte Carlo simulation. Otherwise, we use the observed values of the experiment to do symmetrical global sensitivity analysis. These methods are easy to implement and can reduce the computational cost. An example is given by symmetrical design of experiment.     

Predictive Inference for Big,Spatial, Non‐Gaussian Data: MODIS Cloud Data and its Change‐of‐Support

Remote sensing of the earth with satellites yields datasets that can be massive in size, nonstationary in space, and non‐Gaussian in distribution. To overcome computational challenges, we use the reduced‐rank spatial random effects (SRE) model in a statistical analysis of cloud‐mask data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board NASA's Terra satellite. Parameterisations of cloud processes are the biggest source of uncertainty and sensitivity in different climate models’ future projections of Earth's climate. An accurate quantification of the spatial distribution of clouds, as well as a rigorously estimated pixel‐scale clear‐sky‐probability process, is needed to establish reliable estimates of cloud‐distributional changes and trends caused by climate change. Here we give a hierarchical spatial‐statistical modelling approach for a very large spatial dataset of 2.75 million pixels, corresponding to a granule of MODIS cloud‐mask data, and we use spatial change‐of‐Support relationships to estimate cloud fraction at coarser resolutions. Our model is non‐Gaussian; it postulates a hidden process for the clear‐sky probability that makes use of the SRE model, EM‐estimation, and optimal (empirical Bayes) spatial prediction of the clear‐sky‐probability process. Measures of prediction uncertainty are also given.     

信息技术对审计的影响研究

随着信息技术在社会经济生活中的全方位渗透,适用于传统经济环境的审计理论、方法与技术需要变革以适应现代组织的各类日常运营和活动,通过调研信息技术在审计中的具体应用,从12个方面对信息技术进步与审计发展面临的问题和趋势进行系统性研究。研究认为,信息技术促使审计职能从审计为主变为审计与管控并重,审计范围从经济安全变为经济与信息安全并重,审计内容从财务为主变为财务与业务并重,审计对象从账本变为数据库,审计模式从事后变为事前事中事后全包含,审计方式从现场勘察为主变为远程分析为主,审计介质从纸质变为电子,审计证据从有形变为无形,数据来源从抽样变为全样本,测试内容从人工系统变为信息系统,工作形式从审计合作变为审计协同,风险计算从依赖审计人员经验变为依据系统模型,这些变革极大地推进了审计向现代化方向发展。     

北京主导优势产业链发展战略

本文分析了北京工业发展的优势和劣势以及当前面临的主要问题,在此基础上提出了未来北京工业发展的新思路。作者认为,北京工业发展应面向京津冀和环渤海地区,实行大工业、大市场的一体化战略,即适当控制和强化那些具有突出优势的核心部分,而甩掉或转移扩散那些不具有优势或优势不突出的非核心部分,打造一体化的开放式跨区域主导优势产业链。当前,应着重打造八条一体化的开放式产业链。