Diagnostics in elliptical regression models with stochastic restrictions applied to econometrics

We propose an influence diagnostic methodology for linear regression models with stochastic restrictions and errors following elliptically contoured distributions. We study how a perturbation may impact on the mixed estimation procedure of parameters in the model. Normal curvatures and slopes for assessing influence under usual schemes are derived, including perturbations of case-weight, response variable, and explanatory variable. Simulations are conducted to evaluate the performance of the proposed methodology. An example with real-world economy data is presented as an illustration.     

中国宏观经济波动源的识别与原因分析

为了识别驱动中国宏观经济周期波动性的影响因素,依据中国经济的特殊性,基于1978-2014年42个宏观经济变量的样本数据集构建动态因子模型进行实证分析。研究发现,驱动中国宏观经济波动主要因素有5个潜在宏观因子,其中前四个主要因子分别揭示了驱动中国经济周期波动的主要波动源,它们分别为工业产出因子、外商直接投资(FDI)因子、设备利用率因子和全要素生产率因子。另外,讨论了熨平经济周期性波动的经济政策选择。     

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.     

搅拌釜内双层改进型INTER-MIG桨流场实验研究

为得到双层INTER-MIG桨搅拌釜内流场特征,文章采用粒子图像测速技术(PIV)对直径为430 mm的双层改进 型INTER-MIG桨搅拌槽内流场进行了实验研究。分析了搅拌转速、相位角及轴向位置对搅拌槽内流场分布的影响。结 果表明：整个流场表现出较强轴流特性,底桨右端形成一个漩涡;转速增加引起桨叶附近湍动增强,但整体流型变化不 大;当r/R <0.48时,桨叶间轴流效果显著,有利于釜底流体向上抬升,增强整体混合效果;当r/R>0.48时,桨叶右端在 近壁面处会形成上下2股流动流体,有助于形成局部区域循环。实验结果可为工业搅拌过程提供工艺参考。     

论融资约束下企业最优投资时机选择

利用企业投资的三期静态模型,考察了企业投资额、自有资金与外部融资之间的关系。通过比较企业在不同期间即时投资和延迟投资的投资收益,求解得到企业在融资约束下的投资临界值,进而得出了企业在投资可延迟条件下的最优投资时机和投资规则。     

Inference on a progressive type I interval-censored truncated normal distribution

In this paper, we consider the problem of making statistical inference for a truncated normal distribution under progressive type I interval censoring. We obtain maximum likelihood estimators of unknown parameters using the expectation-maximization algorithm and in sequel, we also compute corresponding midpoint estimates of parameters. Estimation based on the probability plot method is also considered. Asymptotic confidence intervals of unknown parameters are constructed based on the observed Fisher information matrix. We obtain Bayes estimators of parameters with respect to informative and non-informative prior distributions under squared error and linex loss functions. We compute these estimates using the importance sampling procedure. The highest posterior density intervals of unknown parameters are constructed as well. We present a Monte Carlo simulation study to compare the performance of proposed point and interval estimators. Analysis of a real data set is also performed for illustration purposes. Finally, inspection times and optimal censoring plans based on the expected Fisher information matrix are discussed.     

Statistical inference based on generalized Lindley record values

This paper addresses the problems of frequentist and Bayesian estimation for the unknown parameters of generalized Lindley distribution based on lower record values. We first derive the exact explicit expressions for the single and product moments of lower record values, and then use these results to compute the means, variances and covariance between two lower record values. We next obtain the maximum likelihood estimators and associated asymptotic confidence intervals. Furthermore, we obtain Bayes estimators under the assumption of gamma priors on both the shape and the scale parameters of the generalized Lindley distribution, and associated the highest posterior density interval estimates. The Bayesian estimation is studied with respect to both symmetric (squared error) and asymmetric (linear-exponential (LINEX)) loss functions. Finally, we compute Bayesian predictive estimates and predictive interval estimates for the future record values. To illustrate the findings, one real data set is analyzed, and Monte Carlo simulations are performed to compare the performances of the proposed methods of estimation and prediction.     

企业价值评估中自由现金流量分析

自由现金流量是企业价值的基本决定因素,但其定义却随着使用者对现金流量分析目的的不同而有所差异.从企业价值评估的角度对自由现金流量进行分析与定义,给出两种计算自由现金流量的方法,对更好地为企业的现金管理和企业价值评估服务具有重要作用和意义.