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Frontiers in Statistical Quality Control 11

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Cover of 'Frontiers in Statistical Quality Control 11'

Table of Contents

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    Book Overview
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    Chapter 1 Social Network Monitoring: Aiming to Identify Periods of Unusually Increased Communications Between Parties of Interest
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    Chapter 2 Some Recent Results on Monitoring the Rate of a Rare Event
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    Chapter 3 Statistical Perspectives on “Big Data”
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    Chapter 4 Statistical Control of Multiple-Stream Processes: A Literature Review
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    Chapter 5 Regenerative Likelihood Ratio Control Schemes
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    Chapter 6 Variance Charts for Time Series: A Comparison Study
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    Chapter 7 On ARL-Unbiased Control Charts
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    Chapter 8 Optimal Cumulative Sum Charting Procedures Based on Kernel Densities
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    Chapter 9 A Simple Approach for Monitoring Process Mean and Variance Simultaneously
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    Chapter 10 Comparison of Phase II Control Charts Based on Variable Selection Methods
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    Chapter 11 The Use of Inequalities of Camp-Meidell Type in Nonparametric Statistical Process Monitoring
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    Chapter 12 Strategies to Reduce the Probability of a Misleading Signal
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    Chapter 13 Characteristics of Economically Designed CUSUM and \bar{X} Control Charts
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    Chapter 14 SPC of Processes with Predicted Data: Application of the Data Mining Methodology
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    Chapter 15 Shewhart’s Idea of Predictability and Modern Statistics
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    Chapter 16 Sampling Inspection by Variables with an Additional Acceptance Criterion
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    Chapter 17 Fractional Acceptance Numbers for Lot Quality Assurance
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    Chapter 18 Sampling Plans for Control-Inspection Schemes Under Independent and Dependent Sampling Designs with Applications to Photovoltaics
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    Chapter 19 An Overview of Designing Experiments for Reliability Data
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    Chapter 20 Bayesian D-Optimal Design Issues for Binomial Generalized Linear Model Screening Designs
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    Chapter 21 Bayesian Lasso with Effect Heredity Principle
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    Chapter 22 Comparative Study of Time Scales in Optimal Time Scale Analysis of Field Reliability Data
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    Chapter 23 Why the Naive Bayesian Classifier for Clinical Diagnostics or Monitoring Can Dominate the Proper One Even for Massive Data Sets
Attention for Chapter 23: Why the Naive Bayesian Classifier for Clinical Diagnostics or Monitoring Can Dominate the Proper One Even for Massive Data Sets
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Chapter title
Why the Naive Bayesian Classifier for Clinical Diagnostics or Monitoring Can Dominate the Proper One Even for Massive Data Sets
Chapter number 23
Book title
Frontiers in Statistical Quality Control 11
Published by
Springer International Publishing, January 2015
DOI 10.1007/978-3-319-12355-4_23
Book ISBNs
978-3-31-912354-7, 978-3-31-912355-4
Authors

Hans - J. Lenz

Editors

Sven Knoth, Wolfgang Schmid

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 50%
Lecturer 1 25%
Unknown 1 25%
Readers by discipline Count As %
Mathematics 1 25%
Psychology 1 25%
Unknown 2 50%