Show Me the Data: Statistical Representation

Statistical representation is the science and art of using data to describe the world around us. Statistical representation is based on the fundamental concept that data consists of structure plus noise. The challenge facing the statistician is to use the noisy data to learn about the underlying structure. This framework accommodates the analysis of data generated by almost all other scientific disciplines. There are numerous ways of constructing statistical representations. The methods discussed here include tables, graphs, and models. The proper representation depends on the nature of the data and the particular issues being addressed. A combination of methods is often appropriate

Reliability

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

Reliability for the 21st century

The sophistication of science and teclinology is growing almost exponentially. Government and industry are relying more and more on science's advanced methods to assess reliability coupled with pcrformance, safety, surety, cost, schedule, etc. Unfortunately, policy, cost, schedule, and other constraints imposed by the real world inhibit the ability of researchers to calculate these metrics efficiently and accurately using traditional methods. Because of such constraints, reliability must undergo an evolutionary change. The first step in this evolution is to reinterpret the concepts and responsibilities of scientists responsible for reliability calculations to meet the new century's needs. The next step is to mount a multidisciplinary approach to the quantification of reliability and its associated metrics using both empirical methods and auxiliary data sources, such as expert knowledge, corporate memory, and mathematical modeling and simulation