## Industrial Statistics: Aims and Computational Aspects. Proceedings of the Satellite Conference to the 51st Session of the International Statistical Institute (ISI), Athens, Greece, August 16-17, 1997.Christos P. Kitsos, Lutz Edler Devoted to the growing impact of statistical methodology and statistical computing in industry the aim of this book is to link the three components: Statistics - industry - computers. Different areas of industrial statistics are presented by a number of excellent contributions. The following topics are covered: Quality control, engineering and monitoring; reliability and failure time analysis, experimental design; repeated measurements - multiple inference; pharma - statistics; computing, imaging and perception. This book concentrates on the interface between statistical needs in industry and statistical methods developed by statisticians and engineers. |

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### Contents

3 | |

Twoway contingency table with natural orderings in both of rows | 8 |

Experimental Design 149 | 15 |

The Calibration Problem in Industry 17 | 16 |

Case Study of Applying Statistical Techniques for the Quality | 27 |

Taking Multivariate Statistics out into Industry Multivariate | 50 |

An industrial application of PCA and PLS based process | 57 |

Conclusions | 63 |

Robust Inference and Experimental Design for MultiFactor | 164 |

Robust and efficient testing | 171 |

Orthogonal designs and their construction | 177 |

Extended Vrobustness for Twolevel Orthogonal Resolution | 183 |

New Statistical Methods for Analyzing Mutagenicity Assays | 201 |

Application of Statistical Selection Procedures in Biotechnology | 209 |

General remarks on Selection procedures | 210 |

Biotechnology example | 214 |

Breakdown point and consistency | 70 |

Inaccuracy due to incorrect regression model structure | 80 |

Inaccuracy due to random character of the regression | 83 |

Accurate Inferences for the Reliability Function Considering | 99 |

Prediction of Failures that Have Never Occured Exponential Case 111 | 110 |

Statistical model and criteria of reliability used | 112 |

Possible modifications of | 114 |

Bayesian approach | 115 |

NeymanPearsons approach | 119 |

Probability of the all year round service without failures | 120 |

Algorithm of Confidence Limits Calculation for the Probability of the Value 1 of a Monotone Boolean Function of Random Variables | 123 |

Method of confidence limits calculation | 124 |

Algorithm for a calculation of the lower confidence limit | 129 |

Concluding remarks | 133 |

Developing a Graphical Interface for PrePosterior Bayesian Analysis | 135 |

Graphical interface for input and output | 137 |

Bayesian approach to Weibull reliability estimation | 139 |

Application results | 140 |

Concluding remarks | 146 |

NonlinearOptimalSequential Experiment Designs | 151 |

Sequential approach | 157 |

Results and discussion | 219 |

Modeling and Computation in Pharmaceutical Statistics when Analyzing Drug Safety | 221 |

Safety described as Stochastic process | 222 |

State specific safety analysis | 224 |

Longitudinal models for Safety analysis | 227 |

A multicenter randomized trial | 229 |

Discussion | 230 |

Isotonic Inference with Particular Interest in Application to Clinical Trials | 233 |

The case of isotonic inference | 234 |

Various extensions of the monotone relationship | 235 |

A complete class lemma | 236 |

Testing a simple ordered alternative in the normal means | 237 |

Testing ordered alternatives in Binomial Probabilities | 239 |

Tests for Linearity and Tests for Zero Slope in Crossover Studies 243 | 242 |

The FTest for dose dependency | 250 |

A Nonparametric Combination Method for Dependent Permutation | 259 |

Solutions of testing problems | 265 |

Adaptive parametric trend test | 272 |

Convergence Rates of Simulated AnnealingType | 277 |

The image bluring method | 291 |

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Industrial Statistics: Aims and Computational Aspects. Proceedings of the ... Christos Kitsos,Lutz Edler No preview available - 2011 |

### Common terms and phrases

algorithm analysis application approach assumed assumptions asymptotic average Boyadjieva C.P. and Edler calculated calibration problem censored common cause variation computational confidence intervals consider control charts control limits covariance matrix D-optimal data set defined denotes Eds.:Industrial Statistics engineering equation evaluated example experiments factor levels Figure given Heidelberg Kitsos laboratories life-stress relation likelihood function linear model M-estimator maximum likelihood estimator measurements method multivariate statistical mutants Nominal Data non-linear normal distribution number of failures º º observations obtained optimal design order breakdown point Physica-Verlag polynomial population posterior distribution principal components prior information process capability index process performance monitoring process variables Quality Control random effect model random variables regression model reliability safety sample second order breakdown selection procedure sequence SN ratio specification limits statistical process control stochastic stress level subset Table techniques Theorem univariate V-robust vector Vuchkov Weibull zero