Frontiers in Statistical Quality Control 11 (eBook, PDF)

Redaktion: Knoth, Sven; Schmid, Wolfgang

• Format: PDF

Produktbeschreibung

The main focus of this edited volume is on three major areas of statistical quality control: statistical process control (SPC), acceptance sampling and design of experiments. The majority of the papers deal with statistical process control, while acceptance sampling and design of experiments are also treated to a lesser extent.

The book is organized into four thematic parts, with Part I addressing statistical process control. Part II is devoted to acceptance sampling. Part III covers the design of experiments, while Part IV discusses related fields.

The twenty-three papers in this volume stem from The 11^th International Workshop on Intelligent Statistical Quality Control, which was held in Sydney, Australia from August 20 to August 23, 2013. The event was hosted by Professor Ross Sparks, CSIRO Mathematics, Informatics and Statistics, North Ryde, Australia and was jointly organized by Professors S. Knoth, W. Schmid and Ross Sparks. The papers presented here were carefully selected and reviewed by the scientific program committee, before being revised and adapted for this volume.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 393
• Erscheinungstermin: 24. April 2015
• Englisch
• ISBN-13: 9783319123554
• Artikelnr.: 43794119

Inhaltsangabe

Part I - Statistical Process Control: Social Network Monitoring: Aiming to Identify Periods of Unusually Increased Communications Between Parties of Interest by Ross Sparks.- Some Recent Results on Monitoring the Rate of a Rare Event by William H. Woodall and Anne G. Ryan.- Statistical Perspectives on "Big Data" by Fadel M. Megahed and L. Allison Jones-Farmer.- Statistical Control of Multiple-stream Processes - a Literature Review by Eugenio K. Epprecht.- Regenerative Likelihood Ratio Control Schemes by Emmanuel Yashchin.- Variance Charts for Time Series - A Comparison Study by Taras Lazariv and Wolfgang Schmid.- On ARL-unbiased control charts by Sven Knoth and Manuel Cabral Morais.- Optimal Cumulative Sum Charting Procedures Based on Kernel Densities by Jessie Y. Su, Fah Fatt Gan, and Xu Tang.- A Simple Approach for Monitoring Process Mean and Variance Simultaneously by Su-Fen Yang and Barry C. Arnold.- Comparison of Phase II Control Charts Based on Variable Selection Methods by Giovanna Capizzi and Guido Masarotto.- The Use of Inequalities of Camp-Meidell Type in Nonparametric Statistical Process Monitoring by Rainer Göb and Kristina Lurz.- Strategies to Reduce the Probability of a Misleading Signal by Manuel Cabral Morais, Patrícia Ferreira Ramos and António Pacheco.- Characteristics of Economically Designed CUSUM and Control Charts by Erwin Saniga, Darwin Davis, Alireza Faraz, Thomas McWilliams, James

Lucas.- SPC of Processes with Predicted Data - Application of the Data Mining Methodology by Olgierd Hryniewicz.- Shewhart's Idea of Predictability and Modern Statistics by Alessandro Di Bucchianico and Edwin R. van den Heuvel.- Part II - Acceptance Sampling: Sampling Inspection by Variables with an Additional Acceptance Criterion byPeter-Th. Wilrich.- Fractional Acceptance Numbers for Lot Quality by K. Govindaraju and G. Jones.- Sampling Plans for Control-Inspection Schemes Under Independent and Dependent Sampling Designs With Applications to Photovoltaics by Ansgar Steland.- Part III - Design of Experiments: An Overview of Designing Experiments for Reliability Data by G. Geoffrey Vining, Laura J. Freeman, and Jennifer L.K. Kensler.- Bayesian D-Optimal Design Issues for Binomial Generalized Linear Model Screening Designs by Edgar Hassler, Douglas C. Montgomery, and Rachel T. Silvestrini.- Bayesian Lasso with Effect Heredity Principle by Hidehisa Noguchi, Yoshikazu Ojima, Seiichi Yasui.- Part IV - Related Areas: Comparative Study of Time Scales in Optimal Time Scale Analysis of Field Reliability Data by Watalu Yamamoto and Kazuki Takeshita.- Why the Naive Bayesian Classifier for Clinical Diagnostics or Monitoring can Dominate the Proper one Even for Massive Data by Hans - J. Lenz.