Applied Stochastic Models in Business and Industry

Kenett, Ron; Shmueli, Galit

2015 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2105

Kenett, Ron; Shmueli, Galit

2015 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2067

Statistics has long played an important role in impacting the practices of business and industry. As data collection strategies become more automated and first‐principles scientific modeling with computer codes becomes more sophisticated, statistics has the opportunity to evolve and further contribute to the bottom line. Thinking about statistics as a set of tools to be applied piecemeal to a complex problem can be limiting. The emerging discussion about statistical engineering (as proposed by Roger Hoerl and Ronald Snee in Quality Progress, 2010) provides a framework for formalizing the role of statistics in a broader set of applications. Expanding how we think about data collection through resource allocation with multiple possible data types; combining data with first principles models of underlying science and engineering phenomena; and focusing on the multiple facets of the decision‐making process—all represent opportunities to expand the impact and influence of statistics. Statisticians have the opportunity to embrace these new opportunities to expand our sphere of influence and make broader contributions. Examples from collaborative efforts with subject matter experts at Los Alamos National Laboratory are presented to illustrate these emerging areas. Copyright © 2014 John Wiley & Sons, Ltd.

Kenett, Ron; Shmueli, Galit

2015 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2069

Kenett, Ron; Shmueli, Galit

2015 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2070

Kenett, Ron; Shmueli, Galit

2015 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2063

Degradation models have become an important analytic tool for complex systems. During the last two decades, a number of degradation models have been developed to capture the degradation dynamics of a system and aid the subsequent decision‐makings. This paper is aimed at providing a summary of the state of the arts in the field, and discussing some further research issues from both analytical and practical point of view. In this paper, degradation models are classified into three classes, that is, stochastic process models, general path models, and other models beyond these two classes. A review on the three classes is given with emphasis on the class of stochastic process models. A comprehensive comparison between stochastic process models and general path models is given to expound the pros and cons of these two methods. Applications of degradation models in degradation test planning and burn‐in modelling will also be discussed. Copyright © 2014 John Wiley & Sons, Ltd.

Kenett, Ron; Shmueli, Galit

2015 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2078

Kenett, Ron; Shmueli, Galit

2015 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2092

Kenett, Ron; Shmueli, Galit

2015 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2104

The modern service economy is substantively different from the agricultural and manufacturing economies that preceded it. In particular, the cost of experimenting is dominated by opportunity cost rather than the cost of obtaining experimental units. The different economics require a new class of experiments, in which stochastic models play an important role. This article briefly summarizes multi‐armed bandit experiments, where the experimental design is modified as the experiment progresses to reduce the cost of experimenting. Special attention is paid to Thompson sampling, which is a simple and effective way to run a multi‐armed bandit experiment. Copyright © 2015 John Wiley & Sons, Ltd.

Kenett, Ron; Shmueli, Galit

2015 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2099

Kenett, Ron; Shmueli, Galit

2015 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2107