MODa 10-- Advances in model-oriented design and analysis proceedings of the 10th International Workshop in Model-Oriented Design and Analysis held in Łagów Lubuski, Poland, June 10-14, 2013 Dariusz Uciński, Anthony C. Atkinson, Maciej Patan, editors

Format:

Book

Conference/Event

Contributor:

Uciński, Dariusz, 1965-

Atkinson, A. C. (Anthony Curtis)

Patan, Maciej

Conference Name:

International Workshop on Model-Oriented Design and Analysis (10th : 2013 : Łagów Lubuski, Poland) http://viaf.org/viaf/299662350

Series:

Contributions to statistics

Contributions to statistics 1431-1968

Language:

English

Subjects (All):

Experimental design--Congresses.

Experimental design.

Genre:

proceedings (reports)

Conference papers and proceedings

Physical Description:

1 online resource

Place of Publication:

Heidelberg New York Springer ©2013

Language Note:

English

System Details:

text file

PDF

Summary:

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

Contents:

A Convergent Algorithm for Finding KL-Optimum Designs and Related Properties Giacomo Aletti, Caterina May, Chiara Tommasi Robust Experimental Design for Choosing Between Models of Enzyme Inhibition Anthony C. Atkinson, Barbara Bogacka Checking Linear Regression Models Taking Time into Account Wolfgang Bischoff Optimal Sample Proportion for a Two-Treatment Clinical Trial in the Presence of Surrogate Endpoints Atanu Biswas, Buddhananda Banerjee Estimating and Quantifying Uncertainties on Level Sets Using the Vorob'ev Expectation and Deviation with Gaussian Process Models Clément Chevalier, David Ginsbourger Optimal Designs for Multiple-Mixture by Process Variable Experiments Roelof L.J. Coetzer, Linda M. Haines Optimal Design of Experiments for Delayed Responses in Clinical Trials Vladimir Dragalin Construction of Minimax Designs for the Trinomial Spike Model in Contingent Valuation Experiments Ellinor Fackle-Fornius, Linda Wänström

Maximum Entropy Design in High Dimensions by Composite Likelihood Modelling Davide Ferrari, Matteo Borrotti Randomization Based Inference for the Drop-The-Loser Rule Nancy Flournoy, Arkaitz Galbete Adaptive Bayesian Design with Penalty Based on Toxicity-Efficacy Response Lei Gao, William F. Rosenberger Randomly Reinforced Urn Designs Whose Allocation Proportions Converge to Arbitrary Prespecified Values Andrea Ghiglietti, Anna Maria Paganoni Kernels and Designs for Modelling Invariant Functions: From Group Invariance to Additivity David Ginsbourger, Nicolas Durrande Optimal Design for Count Data with Binary Predictors in Item Response Theory Ulrike Graßhoff, Heinz Holling Differences between Analytic and Algorithmic Choice Designs for Pairs of Partial Profiles Heiko Großmann Approximate Bayesian Computation Design (ABCD), an Introduction Markus Hainy, Werner G. Müller Approximation of the Fisher Information Matrix for Nonlinear Mixed Effects Models in Population PK/PD Studies Sergei Leonov, Alexander Aliev

c-Optimal Designs for the Bivariate Emax Model Bergrun Tinna Magnusdottir On the Functional Approach to Locally D-Optimum Design for Multiresponse Models Viatcheslav B. Melas, Lyudmila A. Krylova Sample Size Calculation for Diagnostic Tests in Generalized Linear Mixed Models Tobias Mielke, Rainer Schwabe

D-Optimal Designs for Lifetime Experiments with Exponential Distribution and Censoring Christine H. Müller Convergence of an Algorithm for Constructing Minimax Designs Hans Nyquist Extended Optimality Criteria for Optimum Design in Nonlinear Regression Andrej Pázman, Luc Pronzato Optimal Design for Multivariate Models with Correlated Observations Andrey Pepelyshev Optimal Designs for the Prediction of Individual Effects in Random Coefficient Regression Maryna Prus, Rainer Schwabe

D-Optimum Input Signals for Systems with Spatio-Temporal Dynamics Ewaryst Rafajłowicz, Wojciech Rafajłowicz Random Projections in Model Selection and Related Experimental Design Problems Ewa Skubalska-Rafajłowicz Optimal Design for the Bounded Log-Linear Regression Model HaiYing Wang, Andrey Pepelyshev

Notes:

Includes bibliographical references and index

Online resource; title from digital title page (viewed July 17, 2014)

Other Format:

Print version:

ISBN:

9783319002187

331900218X

OCLC:

835059675

Access Restriction:

Restricted for use by site license