Parameter Estimation of Autoregressive Models Using the Iteratively Robust Filtered Fast-τ Method

Utilizing time series modeling entails estimating the model parameters and dispersion. Classical estimators for autocorrelated observations are sensitive to presence of different types of outliers and lead to bias estimation and misinterpretation. It is important to present robust methods for parameters estimation which are not influenced by contaminations. In this article, an estimation method entitled Iteratively Robust Filtered Fast? τ(IRFFT) is proposed for general autoregressive models. In comparison to other commonly accepted methods, this method is more efficient and has lower sensitivity to contaminations due to having desirable robustness properties. This has been demonstrated by applying MSE, influence function, and breakdown point criteria.     

Major Accidents (Gray Swans) Likelihood Modeling Using Accident Precursors and Approximate Reasoning

Compared to the remarkable progress in risk analysis of normal accidents, the risk analysis of major accidents has not been so well‐established, partly due to the complexity of such accidents and partly due to low probabilities involved. The issue of low probabilities normally arises from the scarcity of major accidents’ relevant data since such accidents are few and far between. In this work, knowing that major accidents are frequently preceded by accident precursors, a novel precursor‐based methodology has been developed for likelihood modeling of major accidents in critical infrastructures based on a unique combination of accident precursor data, information theory, and approximate reasoning. For this purpose, we have introduced an innovative application of information analysis to identify the most informative near accident of a major accident. The observed data of the near accident were then used to establish predictive scenarios to foresee the occurrence of the major accident. We verified the methodology using offshore blowouts in the Gulf of Mexico, and then demonstrated its application to dam breaches in the United Sates.     

清末川康战事——川西藏区改土归流的前奏

清末川边战事近年来成为康藏研究中透视近代边疆社会变迁和帝国向现代民族国家转型的一个焦点,本文围绕清末一系列针对康区寺院（巴塘丁林寺、乡城桑披寺、盐井腊翁寺）的战事展开讨论,对清朝中央与地方政府的治边方略以及康巴地方实力派（寺院、土司）在战术上的回应加以分析。清末川边军事化的过程对后来康区政局的改变起着重要的作用,为改土归流等边疆整合措施作了铺垫。与此同时,川边军事化也引发了四川经济和军事体制的改革,认识清末康区的现代性变革,也应关注四川省政的变化。     

对国民政府后期治藏政策的再分析——以五世热振及沈宗濂关系为中心

五世热振活佛是近代西藏历史上一位倾心内向的活佛,曾为中央政府与西藏地方关系的改善做出过贡献,并戳破了英国人在“西藏问题”上苦心经营的“道德”形象,但热振辞去摄政职务时,国民政府对此的反应却是轻慢的。当１９４２年“外交局”事件发生后,国民政府在治藏政策方面进行了调整。沈宗濂作为蒋介石属意的官员出任蒙藏委员会驻藏办事处处长,曾积极投身藏事,改善了西藏地方与中央的关系,对国民政府的西藏政策发挥了较大影响。但是,２０世纪４０年代末,西藏地方发生了若干事件,国民政府却无所作为。对此,沈宗濂负有难以推卸的责任。然而在沈宗濂态度转变的背后,更本质的原因则是国民政府的消极治藏政策。     

Domino Effect Analysis Using Bayesian Networks

A new methodology is introduced based on Bayesian network both to model domino effect propagation patterns and to estimate the domino effect probability at different levels. The flexible structure and the unique modeling techniques offered by Bayesian network make it possible to analyze domino effects through a probabilistic framework, considering synergistic effects, noisy probabilities, and common cause failures. Further, the uncertainties and the complex interactions among the domino effect components are captured using Bayesian network. The probabilities of events are updated in the light of new information, and the most probable path of the domino effect is determined on the basis of the new data gathered. This study shows how probability updating helps to update the domino effect model either qualitatively or quantitatively. The methodology is applied to a hypothetical example and also to an earlier‐studied case study. These examples accentuate the effectiveness of Bayesian network in modeling domino effects in processing facility.     

利用木耳菌糠栽培元蘑技术的研究

为了解决黑龙江省每年大量木耳菌糠得不到有效利用的实际问题,并积极探索元蘑代料栽培的新技术,以传统栽培种配方CK为对照,以12％的浓度梯度逐渐用菌糠代替木屑设计6个栽培种配方,等量接种生长状况一致的原种,在相同的条件下发菌与出菇.通过采用测定元蘑菌丝的生长势、满袋天数、原基形成时间、成菇状况、子实体的长势及生物学转化效率的方法,研究了栽培料中不同菌糠浓度对元蘑生长周期、生物学效率以及品质的影响.结果表明:栽培种配方为木耳菌糠36％、木屑44％、麦麸17％、黄豆粉1％、玉米面1％、碳酸钙1％,适宜元蘑生长.其菌丝生长浓密健壮,生物学转化效率为81.96％,比对照高出1.74％,试验证明了利用木耳菌糠栽培元蘑是可行的.     

十九世纪滇藏关系解释范式的反思

针对近年来中国边疆与族群研究中的偏重国家与地方精英的二元取向,本文强调汉藏边缘的地方能动性,不仅从边疆的一般性与汉藏边缘的特殊性来揭示族群交汇的特征,也对清末针对少数民族和与少数民族交往的相关政策中的语汇进行分析;在方法上,本文借用"藏彝走廊"的概念探讨西南边疆的族群互动问题。     

Do Shareholders Really Prefer Their Executives to Maximize the Equity Value? A Newsvendor Case*

In this article, we study how the operational decisions of a firm manager depend on her own incentives, the capital structure, and financial decisions in the context of the newsvendor framework. We establish a relationship between the firm’s cost of raising funds and the riskiness of the inventory decisions of the manager. We consider four types of managers, namely, profit, equity, firm value, and profit‐equity maximizers, and initially assume that they may raise funds to increase the inventory level only by issuing debt. We show that the shareholders are indifferent between the different types of managers when the coefficient of variation (CV) of demand is low. However, this is not the case when the CV of demand is high. Based on the demand and the firm’s specific characteristics such as profitability, leverage, and bankruptcy costs, the shareholders might be better off with the manager whose compensation package is tied to the firm value as opposed to the equity value. We, then, extend our model by allowing the manager to raise the required funds by issuing both debt and equity. For this case we focus on the equity and firm value maximizer managers and show that our earlier results (for the debt only case) still hold subject to the cost of issuing equity. However the benefit of the firm value maximizer manager over the equity maximizer manager for shareholders is considerably less in this case compared to the case where the manager can only issue debt. The Board of Directors can take these factors into consideration when establishing/modifying the right incentive package for the managers. We also incorporate the notion of the asymmetric information to capture its impact on the board of directors’ decision about the managers’ incentive package.     

Nonparametric methods for the estimation of imputation uncertainty

It is cleared in recent researches that the raising of missing values in datasets is inevitable. Imputation of missing data is one of the several methods which have been introduced to overcome this issue. Imputation techniques are trying to answer the case of missing data by covering missing values with reasonable estimates permanently. There are a lot of benefits for these procedures rather than their drawbacks. The operation of these methods has not been clarified, which means that they provide mistrust among analytical results. One approach to evaluate the outcomes of the imputation process is estimating uncertainty in the imputed data. Nonparametric methods are appropriate to estimating the uncertainty when data are not followed by any particular distribution. This paper deals with a nonparametric method for estimation and testing the significance of the imputation uncertainty, which is based on Wilcoxon test statistic, and which could be employed for estimating the precision of the imputed values created by imputation methods. This proposed procedure could be employed to judge the possibility of the imputation process for datasets, and to evaluate the influence of proper imputation methods when they are utilized to the same dataset. This proposed approach has been compared with other nonparametric resampling methods, including bootstrap and jackknife to estimate uncertainty in the imputed data under the Bayesian bootstrap imputation method. The ideas supporting the proposed method are clarified in detail, and a simulation study, which indicates how the approach has been employed in practical situations, is illustrated.