Estimation from Zero-Failure Data

When performing quantitative (or probabilistic) risk assessments, it is often the case that data for many of the potential events in question are sparse or nonexistent. Some of these events may be well-represented by the binomial probability distribution. In this paper, a model for predicting the binomial failure probability, P , from data that include no failures is examined. A review of the literature indicates that the use of this model is currently limited to risk analysis of energetic initiation in the explosives testing field. The basis for the model is discussed, and the behavior of the model relative to other models developed for the same purpose is investigated. It is found that the qualitative behavior of the model is very similar to that of the other models, and for larger values of n (the number of trials), the predicted P values varied by a factor of about eight among the five models examined. Analysis revealed that the estimator is nearly identical to the median of a Bayesian posterior distribution, derived using a uniform prior. An explanation of the application of the estimator in explosives testing is provided, and comments are offered regarding the use of the estimator versus other possible techniques.     

基于方差分析的资本结构决策模型

本文利用统计学原理与风险决策分析方法,提出了资本结构决策新的方差分析方法和矩阵模型.与原有的基于概率分析的方法比较,本文建立的模型方法克服和舍弃了模糊性和难操作性,有效地权衡了风险与收益,使资本结构决策模型更富可操作性和广泛适用性,能够为企业最优资本结构决策提供一定的决策依据.     

同种商品不同月份期货组合风险评价模型研究及应用

以期货合约的每一交易日的对数涨跌率来反映市场风险,借助VaR风险价值法,运用加权核估计技术(WKDE)和指数加权滑动模型(EWMA),建立了基于期货组合中持有头寸不同且可以进行风险对冲的期货组合市场风险非线性叠加评价模型,解决了同种商品、不同月份期货组合每一交易日最大损失的确定问题,并通过实证研究验证了模型的实用性.该模型的特点一是借助WKDE法预测组合中单个合约每一交易日涨跌率最大日亏损值,充分体现了期货合约涨跌率的实际走势,使VaR估计更加精确.二是通过动态迁移相关系数矩阵的计算保证了模型的精确性.采用EWMA模型预测动态变化的方差-协方差矩阵,从实证的角度得到更精准的动态迁移相关系数矩阵.三是考虑了组合中多头和空头不同头寸之间的风险对冲,避免了实际中期货组合风险的线性相加而造成放大风险或减少风险的不准确性,从而能较好地保证了模型的预测精度及准确性.四是通过基于风险非线性叠加建立的期货组合风险评价模型解决了SPAN系统中期货组合风险的线性叠加问题,从而得到更合理的组合风险预测值.     

出口二元边际、行业异质性与劳动生产率

基于行业异质性视角,文章主要考察出口二元边际对劳动生产率的影响.利用UN Comtrade数据库,采用HK指数法测度中国出口深度边际和出口广度边际,发现中国出口产品种类基本覆盖了世界全部种类的90％.进一步地,借助聚类分析方法综合多种因素区分行业劳动技能异质性,出口二元边际与劳动生产率关系的实证结果显示:总体上出口深度边际显著推动劳动生产率提高,出口广度边际对劳动生产率产生抑制作用.更为重要的是,出口深度边际对劳动生产率的促进作用仅存在于高技能行业,受限于行业产品技术含量和劳动技能水平等,低技能和中等技能行业出口深度边际出现贫困化增长现象.出口种类扩张的生产率损失超过出口溢出效应的生产率收益,出口广度边际对高中低技能行业劳动生产率具有不同程度的负向影响.     

A new lifetime distribution

Many if not most lifetime distributions are motivated only by mathematical interest. Here, a new three-parameter distribution motivated mainly by lifetime issues is introduced. Some properties of the new distribution including estimation procedures, univariate generalizations and bivariate generalizations are derived. A real data application is described to show its superior performance versus at least that of 15 of the known lifetime models.     

Reliability of stress–strength model for exponentiated Pareto distributions

In this paper, the reliability of a system is discussed when the strength of the system and the stress imposed on it are independent, non-identical exponentiated Pareto distributed random variables. Different point estimations and interval estimations are proposed. The point estimators obtained are maximum likelihood, uniformly minimum variance unbiased and Bayesian estimators. The interval estimations obtained are approximate, exact, bootstrap-p and bootstrap-t confidence intervals and Bayesian credible interval. Different methods and the corresponding confidence intervals are compared using Monte-carlo simulations.     

Generalised Rank Regression Estimator with Standard Error Adjusted Lasso

One of the standard variable selection procedures in multiple linear regression is to use a penalisation technique in least‐squares (LS) analysis. In this setting, many different types of penalties have been introduced to achieve variable selection. It is well known that LS analysis is sensitive to outliers, and consequently outliers can present serious problems for the classical variable selection procedures. Since rank‐based procedures have desirable robustness properties compared to LS procedures, we propose a rank‐based adaptive lasso‐type penalised regression estimator and a corresponding variable selection procedure for linear regression models. The proposed estimator and variable selection procedure are robust against outliers in both response and predictor space. Furthermore, since rank regression can yield unstable estimators in the presence of multicollinearity, in order to provide inference that is robust against multicollinearity, we adjust the penalty term in the adaptive lasso function by incorporating the standard errors of the rank estimator. The theoretical properties of the proposed procedures are established and their performances are investigated by means of simulations. Finally, the estimator and variable selection procedure are applied to the Plasma Beta‐Carotene Level data set.     

Preferences of the Central Bank of Brazil under the inflation targeting regime: Estimation using a DSGE model for a small open economy

The main objective of this paper is to estimate the preferences of the Central Bank of Brazil after the inflation targeting regime (January 2000 to December 2013), using a DSGE model with microeconomic foundations for a small open economy, based especially on the work of Kam et al. (2009). The model used in this study considers that the Central Bank minimizes a loss function, taking into account the deviation of inflation from its target, output stabilization, the interest rate smoothing and, unlike the previous works, the exchange rate. The results show that the major concern of the monetary authority in the period was the stabilization of inflation, followed by interest rate smoothing, exchange stabilization and, finally, output stabilization. The large value for the exchange rate smoothing parameter suggests the presence of fear of floating in the Brazilian case. An improved inflation targeting strategy should allow for less Central Bank intervention in the exchange rate market.     

Construction of Fuzzy Control Charts Based on Weighted Possibilistic Mean

The problem of constructing control charts for fuzzy data has been considered in literature. The proposed transformation approaches and direct fuzzy approaches have their advantages and disadvantages. The representative values charts based on transformation methods are often recommended in practical application. For representing a fuzzy set by a crisp value, the weight of importance of the members assigned with some membership levels in a fuzzy set should be considered, and the possibility theory can be employed to deal with such problem. In this article, we propose to employ the weighted possibilistic mean (WPM), weighted interval valued possibilistic mean (WIVPM) of fuzzy number as a sort of representative values for the fuzzy attribute data, and establish new fuzzy control charts with WPM and WIVPM. The performance of the charts is compared to the existing fuzzy charts with a fuzzy c-chart example via newly defined average number of inspection for variation of control state.