Confidence intervals,significance values,maximum likelihood estimates,etc. sharpened into Occam’s razors

Abstract

Confidence sets, p values, maximum likelihood estimates, and other results of non-Bayesian statistical methods may be adjusted to favor sampling distributions that are simple compared to others in the parametric family. The adjustments are derived from a prior likelihood function previously used to adjust posterior distributions.     

基于时空地理加权回归模型的中国碳排放驱动因素实证研究

基于2006-2011年中国省级区域面板数据,应用时空加权回归模型(GTWR)实证考察了各驱动因素对碳排放规模和碳排放强度影响的时空差异。研究结果表明:大部分解释变量的时空系数估计值显著,波动性较为稳定,符号与预期一致,各驱动因素及其外溢效应在不同区域存在较强的空间异质性,且表现出一定的空间梯度分布。若实现区域差异化碳减排,需要充分考虑空间异质性和外溢性。     

Bayesian approach for optimal PV system sizing under climate change

This paper proposes a novel statistical approach for optimally sizing a stand-alone photovoltaic (PV) system under climate change. Traditionally, the irradiation profile of a typical day or year is used to size PV systems. However, facing the global warming crisis as well as the fact that no two years would have the same weather condition for a single site, this often makes the traditional way failed in the extreme weather conditions. This paper presents a method to statistically model the trend of climate change year by year and put it into the sizing formula, so that the results are optimal for the current weather condition and confidential for the future as well. Hence, the suitable sizes for the PV array and the number of batteries are obtained by pure computation. This is different from the traditional simulation-based sizing curve method. An economic optimization procedure is also presented. In addition to the capital and maintenance costs, a penalty cost is introduced when service fails. A new statistic-based reliability index, the loss of power probability, in terms of threshold-based Extreme Value Theory is presented. This index indicates the upper bound reliability for applications and provides rich information for many extreme events. A technological and economic comparison among the traditional daily energy balance method, sizing curve method and the proposed approach is conducted to demonstrate the usefulness of the new method.     

Assessment of supplier performance based on product-development strategy by applying multi-granularity linguistic term sets

Measuring supplier performance possesses the properties of multi-dimension and multi-scale, it increases the complexity of the aggregation and assessment process. A focal company must develop an effective procedure for overcoming the complexity of performance measurement. Furthermore, the assessment of supplier performance must conform to the product-developed strategy of the focal company. This study is a further research deeper into the concept already introduced in Chang et al. [1] [Applying fuzzy linguistic quantifier to select supply chain partners at different phases of product life cycle. International Journal of Production Economics 2006; 100(2):348–59]; using multi-granularity linguistic variable and numerical ration scale to represent the overall supply performance. By unifying the derived information, the measurement complexity could be resolved. The fuzzy preference was constructed to adjust the consistent direction and transform information into fuzzy relationship. Finally, fuzzy linguistic quantifier guided ordered weighted aggregation (FLQG-OWA) operator with maximal entropy was computed and aggregated with all indicators to meet the current policy of the focal company.     

Uncertainty Analysis Based on Probability Bounds (P-Box) Approach in Probabilistic Safety Assessment

A wide range of uncertainties will be introduced inevitably during the process of performing a safety assessment of engineering systems. The impact of all these uncertainties must be addressed if the analysis is to serve as a tool in the decision-making process. Uncertainties present in the components (input parameters of model or basic events) of model output are propagated to quantify its impact in the final results. There are several methods available in the literature, namely, method of moments, discrete probability analysis, Monte Carlo simulation, fuzzy arithmetic, and Dempster-Shafer theory. All the methods are different in terms of characterizing at the component level and also in propagating to the system level. All these methods have different desirable and undesirable features, making them more or less useful in different situations. In the probabilistic framework, which is most widely used, probability distribution is used to characterize uncertainty. However, in situations in which one cannot specify (1) parameter values for input distributions, (2) precise probability distributions (shape), and (3) dependencies between input parameters, these methods have limitations and are found to be not effective. In order to address some of these limitations, the article presents uncertainty analysis in the context of level-1 probabilistic safety assessment (PSA) based on a probability bounds (PB) approach. PB analysis combines probability theory and interval arithmetic to produce probability boxes (p-boxes), structures that allow the comprehensive propagation through calculation in a rigorous way. A practical case study is also carried out with the developed code based on the PB approach and compared with the two-phase Monte Carlo simulation results.     

加权复合分位数回归方法在动态VaR风险度量中的应用

风险价值(VaR)因为简单直观,成为了当今国际上最主流的风险度量方法之一,而基于时间序列自回归(AR)模型来计算无条件风险度量值在实业界有广泛应用。本文基于分位数回归理论对AR模型提出了一个估计方法--加权复合分位数回归(WCQR)估计,该方法可以充分利用多个分位数信息提高参数估计的效率,并且对于不同的分位数回归赋予不同的权重,使得估计更加有效,文中给出了该估计的渐近正态性质。有限样本的数值模拟表明,当残差服从非正态分布时,WCQR估计的的统计性质接近于极大似然估计,而该估计是不需要知道残差分布的,因此,所提出的WCQR估计更加具有竞争力。此方法在预测资产收益的VaR动态风险时有较好的应用,我们将所提出的理论分析了我国九只封闭式基金,实证分析发现,结合WCQR方法求得的VaR风险与用非参数方法求得的VaR风险非常接近,而结合WCQR方法可以计算动态的VaR风险值和预测资产收益的VaR风险值。     

商业银行集团客户统一授信额度的优化配置研究

企业集团是商业银行重要的贷款客户,商业银行面临企业集团的授信业务风险尤为突出。在结构化模型的框架下,考虑统一授信额度的约束,基于对违约风险控制和贷款收益管理的多目标决策,构建了企业集团成员企业授信额度优化配置模型。示例分析表明,在考虑不同目标重要性的前提下,使用如遗传算法等最优化求解方法,可得到对成员企业授信额度的优化配置方案,从而有助于商业银行积极主动的防范集团客户的信贷风险。     

Modeling Finite‐Time Failure Probabilities in Risk Analysis Applications

In this article, we introduce a framework for analyzing the risk of systems failure based on estimating the failure probability. The latter is defined as the probability that a certain risk process, characterizing the operations of a system, reaches a possibly time‐dependent critical risk level within a finite‐time interval. Under general assumptions, we define two dually connected models for the risk process and derive explicit expressions for the failure probability and also the joint probability of the time of the occurrence of failure and the excess of the risk process over the risk level. We illustrate how these probabilistic models and results can be successfully applied in several important areas of risk analysis, among which are systems reliability, inventory management, flood control via dam management, infectious disease spread, and financial insolvency. Numerical illustrations are also presented.     

概率准则下的投资决策与有效边界间的关系

本文在证券收益率服从正态分布的前提下,对于允许卖空与不允许卖空这两种情况,分别讨论了概率准则下的投资决策与有效边界间的关系,并举例予以了说明。