Data analytics and stochastic modeling in a semiconductor fabBagchi, Sugato; Baseman, Robert J.; Davenport, Andrew; Natarajan, Ramesh; Slonim, Noam; Weiss, Sholom
2010 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.828
The scale, scope and complexity of the manufacturing operations in a semiconductor fab lead to some unique challenges in ensuring product quality and production efficiency. We describe the use of various analytical techniques, based on data mining, process trace data analysis, stochastic simulation and production optimization, to address these manufacturing issues, motivated by the following two objectives. The first objective is to identify the sub‐optimal process conditions or tool settings that potentially affect the process performance and product quality. The second objective is to improve the overall production efficiency through better planning and resource scheduling, in an environment where the product mix and process flow requirements are complex and constantly changing. Copyright © 2010 John Wiley & Sons, Ltd.
Falling and explosive, dormant, and rising markets via multiple‐regime financial time series modelsChen, Cathy W. S.; Gerlach, Richard H.; Lin, Ann M. H.
2010 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.765
A multiple‐regime threshold nonlinear financial time series model, with a fat‐tailed error distribution, is discussed and Bayesian estimation and inference are considered. Furthermore, approximate Bayesian posterior model comparison among competing models with different numbers of regimes is considered which is effectively a test for the number of required regimes. An adaptive Markov chain Monte Carlo (MCMC) sampling scheme is designed, while importance sampling is employed to estimate Bayesian residuals for model diagnostic testing. Our modeling framework provides a parsimonious representation of well‐known stylized features of financial time series and facilitates statistical inference in the presence of high or explosive persistence and dynamic conditional volatility. We focus on the three‐regime case where the main feature of the model is to capturing of mean and volatility asymmetries in financial markets, while allowing an explosive volatility regime. A simulation study highlights the properties of our MCMC estimators and the accuracy and favourable performance as a model selection tool, compared with a deviance criterion, of the posterior model probability approximation method. An empirical study of eight international oil and gas markets provides strong support for the three‐regime model over its competitors, in most markets, in terms of model posterior probability and in showing three distinct regime behaviours: falling/explosive, dormant and rising markets. Copyright © 2009 John Wiley & Sons, Ltd.
Sensitivity analysis of the moments of the profit on an Income Protection PolicyCordeiro, Isabel Maria; Magalhães, Pedro Manuel
2010 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.766
The main purpose of this paper is to perform a sensitivity analysis where we quantify and analyse the effects on the mean of the profit on an Income Protection policy and two risk measures of changing the values of the transition intensities. All the calculations carried out are based on a multiple state model for Income Protection proposed in Continuous Mortality Investigation Committee (Continuous Mortality Investigation Reports 1991; 12). Within this model, we derive a formula for the mean of the profit, which enables to evaluate it more efficiently. In order to calculate the two risk measures we use the numerical algorithms for the calculation of the moments of the profit proposed by Waters (Insurance: Mathematics and Economics 1990; 9:101–113). We carry out the sensitivity analysis considering two different situations: in the first situation, we update the premium rates used to calculate the moments of the profit, according to the changes in the values of the transition intensities; in the second one, we do not update the premium rates. Both analyses are of practical interest to insurance companies selling Income Protection policies. Copyright © 2009 John Wiley & Sons, Ltd.
Mining performance data through nonlinear PCA with optimal scalingCostantini, Paola; Linting, Marielle; Porzio, Giovanni C.
2010 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.771
Performance data are usually collected in order to build well‐defined performance indicators. Since such data may conceal additional information, which can be revealed by secondary analysis, we believe that mining of performance data may be fruitful. We also note that performance databases usually contain both qualitative and quantitative variables for which it may be inappropriate to assume some specific (multivariate) underlying distribution. Thus, a suitable technique to deal with these issues should be adopted. In this work, we consider nonlinear principal component analysis (PCA) with optimal scaling, a method developed to incorporate all types of variables, and to discover and handle nonlinear relationships. The reader is offered a case study in which a student opinion database is mined. Though generally gathered to provide evidence of teaching ability, they are exploited here to provide a more general performance evaluation tool for those in charge of managing universities. We show how nonlinear PCA with optimal scaling applied to student opinion data enables users to point out some strengths and weaknesses of educational programs and services within a university. Copyright © 2009 John Wiley & Sons, Ltd.