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    Reliability

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    This special volume of Statistical Sciences presents some innovative, if not provocative, ideas in the area of reliability, or perhaps more appropriately named, integrated system assessment. In this age of exponential growth in science, engineering and technology, the capability to evaluate the performance, reliability and safety of complex systems presents new challenges. Today's methodology must respond to the ever-increasing demands for such evaluations to provide key information for decision and policy makers at all levels of government and industry--problems ranging from international security to space exploration. We, the co-editors of this volume and the authors, believe that scientific progress in reliability assessment requires the development of processes, methods and tools that combine diverse information types (e.g., experiments, computer simulations, expert knowledge) from diverse sources (e.g., scientists, engineers, business developers, technology integrators, decision makers) to assess quantitative performance metrics that can aid decision making under uncertainty. These are highly interdisciplinary problems. The principal role of statistical sciences is to bring statistical rigor, thinking and methodology to these problems.Comment: Published at http://dx.doi.org/10.1214/088342306000000664 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

    Bayesian modeling for composite reliability and maximal reliability.

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    A reliability coefficient in psychometrics is used as an index of consistency. The α coefficient has been widely used as an estimate of reliability coefficient: however, in recent years, there has been an increasing interest in devising other methods of estimating reliability. I have made extensive revisions to enhance clarity and reduce redundancy. In addition to reporting the point estimate of the reliability coefficient, it is also recommended to report the results of interval estimation. Furthermore, psychological research using Bayesian modeling is gradually gaining popularity. In this paper, we introduce a Bayesian model for obtaining the point and interval estimation of maximal reliability and ω coefficient using a statistical analysis environment R and Stan that implements HMC sampling.信頼性係数は心理尺度開発場面で、尺度の安定の度合いを示す指標として利用されている。信頼性係数の代表的な指標としてα係数が広く利用されてきた。近年、α係数の再検討が進み、その他の信頼性係数の指標にも関心が高まっている。また、信頼性係数の報告も点推定値のみならず、区間推定を行った結果を報告する事も意識されるようになっている。更に、ベイズモデリングを利用した心理学研究が増えつつある。本稿では統計解析環境RおよびHMCサンプリングを実装したStanを用いて、ベイズモデリングによって最大信頼性およびω係数の推定値と確信区間を構成する方法を紹介する

    Forward Reliability Markets: Less Risk, Less Market Power, More Efficiency

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    A forward reliability market is presented. The market coordinates new entry through the forward procurement of reliability options—physical capacity bundled with a financial option to supply energy above a strike price. The market assures adequate generating resources and prices capacity from the bids of competitive new entry in an annual auction. Efficient performance incentives are maintained from a load-following obligation to supply energy above the strike price. The capacity payment fully hedges load from high spot prices, and reduces supplier risk as well. Market power is reduced in the spot market, since suppliers enter the spot market with a nearly balanced position in times of scarcity. Market power in the reliability market is addressed by not allowing existing supply to impact the capacity price. The approach, which has been adopted in New England and Colombia, is readily adapted to either a thermal or a hydro system.Auctions, electricity auctions, capacity auctions, reliability auctions

    Reliability Analysis Model

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    RAM program determines probability of success for one or more given objectives in any complex system. Program includes failure mode and effects, criticality and reliability analyses, and some aspects of operations, safety, flight technology, systems design engineering, and configuration analyses
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