Christina M. Mastrangelo

Multilevel Statistical Models, 4th edition

In summary, the book presents many excellent Bayesian hierarchical modeling techniques to tackle difficult and realistic modeling issues that many researchers may encounter in their scientific areas. For example, by constructing appropriate structured errors, the dependency over temporal and spatial data can be well dealt with. For example, data collected in multistage manufacturing process over time and different manufacturing stations and repeatedly measured performance degradation data can be well modeled using Bayesian hierarchical models introduced in this book. I think that the modeling techniques will not only benefit researchers in health and social sciences, but also those in engineering, marketing, and financial fields where complicated and correlated data appear frequently. Thus, the book would be an excellent collection and reference for researchers who are interested in applying the most recent Bayesian hierarchical modeling methods to their own areas.

Integrated Process Control for Startup Operations

We focus on transient period monitoring and adjustment of systems undergoing a startup operation (e.g., a new production run or resuming production after shut-down, etc.). We design an integrated process control mechanism based on a proportional-integra..

Intelligent decision support systems

We examine characteristics common to successful intelligent decision support systems. In doing this, we attempt to bridge the gap between disparate communities engaged in building various parts of these systems. Three systems were examined in detail from widely different applications and more than 20 additional systems were considered at a lower level of detail. By examining deployed decision support systems within the context of a broad framework we hope to capture the characteristics that can guide future development efforts. We see this as a first step in developing an in-depth compendium that will help bridge the gap between important yet typically isolated fields.

A FAST INITIAL RESPONSE SCHEME FOR THE EXPONENTIALLY WEIGHTED MOVING AVERAGE CONTROL CHART

Capability indices are becoming increasingly popular in industry. One of the problems associated with these indices is the underlying assumption of normality. Four non-normal distributions were examined for their effect on inferences made using the stan..

Multivariate autocorrelated processes: Data and shift generation

The comparison of out-of-control performance of multivariate control chart methods on autoregressive processes requires a consistent method of generating a multivariate process shift. By applying the shift to the mean vector of the noise series, the covariance structure of the data may be maintained. We present a program for generating multivariate autoregressive data with a shift in the mean vector of the noise series. The program can be used to generate multivariate data from a first order vector autoregressive model with a shift in the mean vector of the noise series. The data can then be used to compare the shift detection properties of multivariate control chart methods.

Shift Detection Properties of Moving Centerline Control Chart Schemes

In statistical process monitoring, violating the assumption of independent data results in a control chart that exhibits increased false alarms and trends on both sides of the centerline. Autocorrelation requires modification to traditional control chart techniques. This paper explores the shift detection capability of the moving centerline exponentially weighted moving average (MCEWMA) chart and recommends enhancements for quicker detection of small process upsets.

Monitoring Serially-Dependent Processes with Attribute Data

A situation in which unacceptable variability in a product at one point in a production sequence causes unacceptable variability in a product at the succeeding station is discussed. Traditional methods of statistical process control are ineffectiv..

Addressing multicollinearity in semiconductor manufacturing

When building prediction models in the semiconductor environment, many variables, such as input/output variables, have causal relationships which may lead to multicollinearity. There are several approaches to address multicollinearity: variable elimination, orthogonal transformation, and adoption of biased estimates. This paper reviews these methods with respect to an application that has a structure more complex than simple pairwise correlations. We also present two algorithmic variable elimination approaches and compare their performance with that of the existing principal component regression and ridge regression approaches in terms of residual mean square and R2. Copyright

Application of Generalized Linear Models to Predict Semiconductor Yield Using Defect Metrology Data

Semiconductor yield modeling is essential to identify processing issues, improve quality, and meet customer demand. However, the massive amounts of data collected during the fabrication process and the number of historical models available make yield modeling a complex and challenging task. This paper presents a methodology to guide the practitioner in determining what data should be collected, integrated, and aggregated, followed by a modeling strategy to forecast yield using generalized linear models based on defect metrology data. This technique yields results at both the die and the wafer levels, significantly outperforms existing models found in the literature based on prediction errors, and identifies significant factors that can drive process improvement. This method also allows the nested structure of the process to be considered in the model, improving predictive capabilities and violating fewer assumptions. An example is presented to discuss this approach and to demonstrate the advantages of these models over the models of the past.

A Case Study in Monitoring Hospital-Associated Infections with Count Control Charts

ABSTRACT Hospital-associated infections are a major concern in the medical community due to the potential loss of life and high costs. Monitoring the incidences of infections is an established part of quality maintenance programs in hospitals. However, traditional methods of analysis are often inadequate since the incidences of infections are infrequent. In order to address this issue, techniques such as the cumulative sum (CUSUM) chart for counted data and the g-type control chart have been suggested. This article demonstrates how these charts may be applied to infection control surveillance data from Childrens Hospital and makes recommendations for a control chart most suitable for monitoring hospital-associated infections.