h - Ucinski, Dariusz; Atkinson, Anthony C.; Patan, Maciej; SpringerLink (Online service) (Heidelberg : Springer International Publishing : Imprint: Springer, 2013)
This book collects the proceedings of the 10th Workshop on Model-Oriented Design and Analysis (mODa). A model-oriented view on the design of experiments, which is the unifying theme of all mODa meetings, assumes some knowledge of the form of the data-generating process and naturally leads to the so-called optimum experimental design. Its theory and practice have since become important in many scientific and technological fields, ranging from optimal designs for dynamic models in pharmacological research, to designs for industrial experimentation, to designs for simulation experiments in environmental risk management, to name but a few. The methodology has become even more important in recent years because of the increased speed of scientific developments, the complexity of the systems currently under investigation and the mounting pressure on businesses, industries and scientific researchers to reduce product and process development times. This increased competition requires ever increasing efficiency in experimentation, thus necessitating new statistical designs. This book presents a rich collection of carefully selected contributions ranging from statistical methodology to emerging applications. It primarily aims to provide an overview of recent advances and challenges in the field, especially in the context of new formulations, methods and state-of-the-art algorithms. The topics included in this volume will be of interest to all scientists and engineers and statisticians who conduct experiments.
A Convergent Algorithm for Finding KL-Optimum Designs and Related Properties -- Robust Experimental Design for Choosing Between Models of Enzyme Inhibition -- Checking Linear Regression Models Taking Time into Account -- Optimal Sample Proportion for a Two-Treatment Clinical Trial in the Presence of Surrogate Endpoints -- Estimating and Quantifying Uncertainties on Level Sets Using the Vorobev Expectation and Deviation with Gaussian Process Models -- Optimal Designs for Multiple-Mixture by Process Variable Experiments -- Optimal Design of Experiments for Delayed Responses in Clinical Trials -- Construction of Minimax Designs for the Trinomial Spike Model in Contingent Valuation Experiments -- Maximum Entropy Design in High Dimensions by Composite Likelihood Modelling -- Randomization Based Inference for the Drop-The-Loser Rule -- Adaptive Bayesian Design with Penalty Based on Toxicity-Efficacy Response -- Randomly Reinforced Urn Designs Whose Allocation Proportions Converge to Arbitrary Prespecified Values -- Kernels and Designs for Modelling Invariant Functions: From Group Invariance to Additivity -- Optimal Design for Count Data with Binary Predictors in Item Response Theory -- Differences between Analytic and Algorithmic Choice Designs for Pairs of Partial Profiles -- Approximate Bayesian Computation Design (ABCD), An Introduction -- Approximation of the Fisher Information Matrix for Nonlinear Mixed Effects Models in Population Pk/Pd Studies -- c-Optimal Designs for the Bivariate Emax Model -- On the Functional Approach to Locally D-Optimum Design for Multiresponse Models -- Sample Size Calculation for Diagnostic Tests in Generalized Linear Mixed Models -- D-Optimal Designs for Lifetime Experiments with Exponential Distribution and Censoring -- Convergence of An Algorithm for Constructing Minimax Designs -- Extended Optimality Criteria for Optimum Design in Nonlinear Regression -- Optimal Design for Multivariate Models with Correlated Observations -- Optimal Designs for the Prediction of Individual Effects in Random Coefficient Regression -- D-Optimum Input Signals for Systems with Spatio-Temporal Dynamics -- Random Projections in Model Selection and Related Experimental Design Problems -- Optimal Design for the Bounded Log-Linear Regression Model.
Keywords: Statistics; Mathematical statistics; Statistics; Statistical Theory and Methods; Statistics for Life Sciences, Medicine, Health Sciences; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Statistics and Computing/Statistics Programs