## MODA 5 - Advances in Model-Oriented Data Analysis and Experimental Design: Proceedings of the 5th International Workshop in Marseilles, France, June 22–26, 1998Anthony C. Atkinson, Luc Pronzato, Henry P. Wynn This volume contains the majority of the papers presented at the 5th Inter national Workshop on Model-Oriented Data Analysis held in June 1998. This series started in March 1987 with a meeting on the Wartburg, Eisenach (Germany). The next three meetings were in 1990 (St Kyrik monastery, Bulgaria), 1992 (Petrodvorets, StPetersburg, Russia) and 1995 (Spetses, Greece). The main purpose of these workshops was to bring together lead ing scientists from 'Eastern' and 'Western' Europe for the exchange of ideas in theoretical and applied statistics, with special emphasis on experimen tal design. Now that the separation between East and West has become less rigid, this dialogue has, in principle, become much easier. However, providing opportunities for this dialogue is as vital as ever. MODA meetings are known for their friendly atmosphere, leading to fruitful discussions and collaboration, especially between young and senior scien tists. Indeed, many long term collaborations were initiated during these events. This intellectually stimulating atmosphere is achieved by limiting the number of participants to around eighty, by the choice of location so that participants can live as a community, and, of course, through the care ful selection of scientific direction made by the Programme Committee. |

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### Contents

3 | |

EOptimal Designs for the Double Exponential Model | 10 |

On Robust Estimation of a Correlation Coefficient | 17 |

Comparison of Spectral and Hadamard Bounds | 21 |

Characteristic Polynomial Criteria in Optimal Experimen | 31 |

Conclusion | 48 |

Optimal Designs for Models with Ignored Heteroscedastic | 59 |

Selective Random Search for Optimal Experiment Designs | 75 |

Designing Experiments for Adaptively Fitted Models | 165 |

Testing Genetic Parameters in the Mixed Model of Triallel | 176 |

NonParametric Search for Significant Variables of a Linear | 187 |

TwoStage Designs for Model Discrimination and Parame | 195 |

Determination of the Size of an Experiment | 206 |

Selection of the Most | 215 |

The Generalized 3Method in Taguchi Experiments | 223 |

Some Statistical Properties of Nested Block Designs | 231 |

Asymptotic Upper Bounds for the Optimal Design Length | 84 |

On the Equal Allocation Rules in Quantal DoseResponse | 93 |

PARAMETER ESTIMATION | 103 |

Interval Analysis for Guaranteed Nonlinear Parameter | 115 |

rameters | 122 |

Breakdown Points of Estimators for Aspects of Linear | 136 |

Approximate Densities of Two BiasCorrected Nonlinear | 145 |

A New Interpretation of Design Measures | 239 |

Risk Based Optimal Designs P Sebastiani H P Wynn | 247 |

Quality Improvement Through Mechanistic Models | 258 |

THE TEACHING OF EXPERIMENTAL DESIGN | 271 |

Discussion on Teaching Experimental Design | 283 |

273 | 297 |

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### Common terms and phrases

algorithm analysis approximation assume asymptotic balance weighing design binary block design breakdown point chemical balance weighing compound optimal design consider convex covariance covariance matrix criterion D-optimal defined denote density design of experiments distribution efficiency function eigenvalue entropy equal allocation rules equation error example experimental design experiments Fedorov Fisher information matrix given global heteroscedastic homoscedastic inclusion function information matrix interval KEYWORDS least squares estimator Lemma linear model linear regression Markov chains maximal mean squared error Melas minimal Müller MV-optimal NB design observations obtained optimal design optimality criteria optimum paper Pázman polynomial regression problem proof Pukelsheim random regression models robust estimators sample Section sequential solution spectral bound standardized E-optimal design Statistics support points symmetric Theorem Torsney two-stage uniform equal allocation upper bound values variability control factor vector weight function Wynn