Now showing items 14-33 of 39

    • High Breakdown Estimation Methods for Phase I Multivariate Control Charts 

      Jensen, Willis A.; Birch, Jeffrey B.; Woodall, William H. (Virginia Tech, 2005)
      The goal of Phase I monitoring of multivariate data is to identify multivariate outliers and step changes so that the estimated control limits are sufficiently accurate for Phase II monitoring. High breakdown estimation ...
    • An Improved Genetic Algorithm Using a Directional Search 

      Wan, Wen; Birch, Jeffrey B. (Virginia Tech, 2009)
      The genetic algorithm (GA), a very powerful tool used in optimization, has been applied in various fields including statistics. However, the general GA is usually computationally intensive, often having to perform a large ...
    • An Improved Hybrid Genetic Algorithm with a New Local Search Procedure 

      Wan, Wen; Birch, Jeffrey B. (Virginia Tech, 2012)
      A hybrid genetic algorithm (HGA) combines a genetic algorithm (GA) with an individual learning procedure. One such learning procedure is a local search technique (LS) used by the GA for refining global solutions. A HGA is ...
    • Interaction Analysis of Three Combination Drugs via a Modified Genetic Algorithm 

      Wan, Wen; Pei, Xin-Yan; Grant, Steven; Birch, Jeffrey B.; Felthousen, Jessica; Dai, Yun; Fang, Hong-Bin; Tan, Ming; Sun, Shumei (Virginia Tech, 2014)
      Few articles have been written on analyzing and visualizing three-way interactions between drugs. Although it may be quite straightforward to extend a statistical method from two-drugs to three-drugs, it is hard to visually ...
    • An Investigation of Combinations of Multivariate Shewhart and MEWMA Control Charts for Monitoring the Mean Vector and Covariance Matrix 

      Reynolds, Marion R. Jr.; Stoumbos, Zachary G. (Virginia Tech, 2008-01-22)
      When monitoring a process which has multivariate normal variables, the Shewhart-type control chart (Hotelling (1947)) traditionally used for monitoring the process mean vector is effective for detecting large shifts, but ...
    • Linear Mixed Model Robust Regression 

      Waterman, Megan J.; Birch, Jeffrey B.; Schabenberger, Oliver (Virginia Tech, 2006-11-05)
      Mixed models are powerful tools for the analysis of clustered data and many extensions of the classical linear mixed model with normally distributed response have been established. As with all parametric models, correctness ...
    • Model Robust Calibration: Method and Application to Electronically-Scanned Pressure Transducers 

      Walker, Eric L.; Starnes, B. Alden; Birch, Jeffrey B.; Mays, James E. (American Institute of Aeronautics and Astronautics, 2010)
      This article presents the application of a recently developed statistical regression method to the controlled instrument calibration problem. The statistical method of Model Robust Regression (MRR), developed byMays, Birch, ...
    • Monitoring Markov Dependent Observations with a Log-Likelihood Based CUSUM 

      Mousavi, Shabnam; Reynolds, Marion R. Jr. (Virginia Tech, 2008)
      When control charts are used to monitor a proportion p it is traditionally assumed that the binary observations are independent. The work that has been done on monitoring autocorrelated binary observations has assumed a ...
    • Nonparametric and Semiparametric Linear Mixed Models 

      Waterman, Megan J.; Birch, Jeffrey B.; Abdel-Salam, Abdel-Salam G. (Virginia Tech, 2012)
      Mixed models are powerful tools for the analysis of clustered data and many extensions of the classical linear mixed model with normally distributed response have been established. As with all parametric models, correctness ...
    • Nonparametric and Semiparametric Mixed Model Methods for Phase I Profile Monitoring 

      Abdel-Salam, Abdel-Salam G.; Birch, Jeffrey B.; Jensen, Willis A. (Virginia Tech, 2010)
      Profile monitoring is an approach in quality control best used where the process data follow a profile (or curve). The majority of previous studies in profile monitoring focused on the parametric modeling of either linear ...
    • On Computing the Distribution Function for the Sum of Independent and Non-identical Random Indicators 

      Hong,Yili (Virginia Tech, 2011-04-05)
      The Poisson binomial distribution is the distribution of the sum of independent and non-identical random indicators. Each indicator follows a Bernoulli distribution with individual success probability. When all success ...
    • On the Distribution of Hotelling's T² Statistic Based on the Successive Differences Covariance Matrix Estimator 

      Williams, James D.; Woodall, William H.; Birch, Jeffrey B.; Sullivan, Joe H. (Virginia Tech, 2004-09-30)
      In the historical (or retrospective or Phase I) multivariate data analysis, the choice of the estimator for the variance-covariance matrix is crucial to successfully detecting the presence of special causes of variation. ...
    • Outlier Robust Nonlinear Mixed Model Estimation 

      Williams, James D.; Birch, Jeffrey B.; Abdel-Salam, Abdel-Salam G. (Virginia Tech, 2014)
      In standard analyses of data well-modeled by a nonlinear mixed model (NLMM), an aberrant observation, either within a cluster, or an entire cluster itself, can greatly distort parameter estimates and subsequent standard ...
    • A Phase I Cluster-Based Method for Analyzing Nonparametric Profiles 

      Chen, Yajuan; Birch, Jeffrey B.; Woodall, William H. (Virginia Tech, 2014)
      A cluster-based method was used by Chen et al.²⁴ to analyze parametric profiles in Phase I of the profile monitoring process. They showed performance advantages in using their cluster-based method of analyzing parametric ...
    • Profile Monitoring via Linear Mixed Models 

      Jensen, Willis A.; Birch, Jeffrey B.; Woodall, William H. (Virginia Tech, 2006)
      Profile monitoring is a relatively new technique in quality control used when the product or process quality is best represented by a profile (or a curve) at each time period. The essential idea is often to model the profile ...
    • Profile Monitoring via Nonlinear Mixed Models 

      Jensen, Willis A.; Birch, Jeffrey B. (Virginia Tech, 2006)
      Profile monitoring is a relatively new technique in quality control best used where the process data follows a profile (or curve) at each time period. Little work has been done on the monitoring on nonlinear profiles. ...
    • Robust Parameter Design: A Semi-Parametric Approach 

      Pickle, Stephanie M.; Robinson, Timothy J.; Birch, Jeffrey B.; Anderson-Cook, Christine M. (Virginia Tech, 2005)
      Parameter design or robust parameter design (RPD) is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The goal of parameter design is to choose the levels ...
    • A Semiparametric Approach to Dual Modeling 

      Robinson, Timothy J.; Birch, Jeffrey B.; Starnes, B. Alden (Virginia Tech, 2006)
      In typical normal theory regression, the assumption of homogeneity of variances is often not appropriate. When heteroscedasticity exists, instead of treating the variances as a nuisance and transforming away the heterogeneity, ...
    • A Semiparametric Technique for the Multi-Response Optimization Problem 

      Wan, Wen; Birch, Jeffrey B. (Virginia Tech, 2009)
      Multi-response optimization (MRO) in response surface methodology (RSM) is quite common in applications. Before the optimization phase, appropriate fitted models for each response are required. A common problem is model ...
    • Speculations Concerning the First Ultraintelligent Machine 

      Good, Irving John (Virginia Tech, 2005-03-05)
      The survival of man depends on the early construction of an ultraintelligent machine. In order to design an ultraintelligent machine we need to understand more about the human brain or human thought or both. In the following ...