A random process (field) with given parametrized mean and covariance function is observed at a finite number of chosen design points . The information about its parameters is measured via the Fisher information matrix (for normally distributed observations) or using information functionals depending on that matrix. Conditions are stated, under which the contribution of one design point to this ...

March 20, 2015 Type Package Title Population (and Individual) Optimal Experimental Design Version 0.2.0 Depends ggplot2 Imports MASS, mvtnorm, nlme, dplyr, codetools Suggests testthat, Hmisc Description Optimal experimental designs for both population and individual studies based on nonlinear mixed-effect models. Often this is based on a computation of the Fisher Information Matrix (FIM). This ...

We address the problem of robust, computationally-efficient design of biological experiments. Classical optimal experiment design methods have not been widely adopted in biological practice, in part because the resulting designs can be very brittle if the nominal parameter estimates for the model are poor, and in part because of computational constraints. We present a method for robust experime...

Viscoelastic materials can today be found in a wide range of practical applications. In order to make efficient use of these materials in construction, it is of importance to know how they behave when subjected to dynamic load. Characterization of viscoelastic materials is therefore an important topic, that has received a lot of attention over the years. This thesis treats different methods for...

In the common linear and quadratic regression model with an autoregressive error structure exact D-optimal designs for weighted least squares analysis are determined. It is demonstrated that for highly correlated observations the Doptimal design is close to the equally spaced design. Moreover, the equally spaced design is usually very efficient, even for moderate sizes of the correlation, while...

S INCE Kiefer (1959) debuted the idea of optimal design of experiments, a vast literature has grown up around the notion of choosing a design based on some numerical criterion. The most common is D-optimality, which chooses the design minimizing the generalized variance of the regression parameter estimates. Though standard designs can be used in most design situations, optimal procedures are u...

Process models play important role in computer aided process engineering. Although the structure of these models are a priori known, model parameters should be estimated based on experiments. The accuracy of the estimated parameters largely depends on the information content of the experimental data presented to the parameter identification algorithm. Optimal experiment design (OED) can maximiz...

The optimum experimental design for systems following the ®rst-order Arrhenius model under linearly increasing temperature pro®les was studied by determining the sampling conditions that lead to a minimum con®dence region of the model parameters. It was found that experiments should be started at the lowest possible temperature and, for each initial temperature, there is an optimal heating rate...

Manipulated variable moves are computed to maximize the information contained in experimental data collected from sheet and film processes. The experimental design procedure minimizes the confidence ellipsoid of the critical model parameters over the manipulated variables subject to their physical constraints. For a simulated blown film process, the model is 1 order of magnitude more accurate t...

Applications of the theory of optimal design of experiments to radial velocity planet search surveys are considered. Different optimality criteria are discussed, basing on the Fisher, Shannon, and Kullback-Leibler informations. Algorithms of optimal scheduling of RV observations for two important practical problems are considered. The first problem is finding the time for future observations to...