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Showing 1 to 13 of 13 for “"design optimality"”.
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Frequency selective analog to digital converter design : optimality, fundamental limitations, and performance bounds
In this thesis, the problem of analysis and design of Analog to Digital Converters (ADCs) is studied within an optimal feedback control framework. A general ADC is modeled as a causal, discrete-time dynamical system with outputs taking values in a finite set. The performance measure is defined as …
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Optimal Blocking for Three Treatments and BIBD Robustness - Two Problems in Design Optimality
Design optimality plays a central role in the area of statistical experimental design. In general, problems in design optimality are composed of two vital, but separable, components. One of these is determining conditions under which a design is optimal (such as criterion bounds, values of design …
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Weighted Optimality of Block Designs
Design optimality for treatment comparison experiments has been intensively studied by numerous researchers, employing a variety of statistically sound criteria. Their general formulation is based on the idea that optimality functions of the treatment information matrix are invariant to treatment …
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Measurement Error in Designed Experiments for Second Order Models
Measurement error (ME) in the factor levels of designed experiments is often overlooked in the planning and analysis of experimental designs. A familiar model for this type of ME, called the Berkson error model, is discussed at length. Previous research has examined the effect of Berkson error on …
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Design for Additive Manufacturing: Simultaneous Optimization of Structural Integrity and Minimal Support Structures
… (TO): the difficulty in fabricating its complex designs, especially for Additive Manufacturing (AM), and its significant computational costs. It develops a unified design framework that directly embeds AM constraints such as overhang angles and build direction into robust TO formulations. To …
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Optimal one and two-stage designs for the logistic regression model
… regression model, a well known nonlinear model. Designing an optimal experiment for this nonlinear situation poses some problems not encountered with a linear model. The application of several optimality design criteria to the logistic regression model is explored, and many resulting optimal …
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Bayesian Two Stage Design Under Model Uncertainty
Traditional single stage design optimality procedures can be used to efficiently generate data for an assumed model y = f(x<sup>(m)</sup>,b) + ε. The model assumptions include the form of f, the set of regressors, x<sup>(m)</sup> , and the distribution of ε. The nature of the response, y, often …
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Optimal Experimental Design for Poisson Impaired Reproduction Studies
… on the development of efficient experimental designs for impaired reproduction studies. This research concentrates on two areas: 1) the use of Bayesian techniques to make single regressor designs robust to parameter misspecification and 2) the extension of design optimality methods to the …
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Bayesian D-Optimal Design for Generalized Linear Models
Bayesian optimal designs have received increasing attention in recent years, especially in biomedical and clinical trials. Bayesian design procedures can utilize the available prior information of the unknown parameters so that a better design can be achieved. However, a difficulty in dealing with …
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Understanding Scaled Prediction Variance Using Graphical Methods for Model Robustness, Measurement Error and Generalized Linear Models for Response Surface Designs
… are becoming important tools for evaluating designs. The need to compare designs in term of their prediction variance properties advanced this development. A recent graphical tool, the Fraction of Design Space plot, is useful to calculate the fraction of the design space where the scaled …
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Optimal Experimental Designs for the Poisson Regression Model in Toxicity Studies
Optimal experimental designs for generalized linear models have received increasing attention in recent years. Yet, most of the current research focuses on binary data models especially the one-variable first-order logistic regression model. This research extends this topic to count data models. …
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Multiscale Design And Life-Cycle Based Sustainability Assessment Of Polymer Nanocomposite Coatings
… correlations of nanocoating materials and their design complexity. Experimental efforts have been successful in developing certain types of nanopaints exhibiting improved properties. However, multifunctional nanopaint design optimality is extremely difficult to address if not impossible solely …
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On the Efficiency of Designs for Linear Models in Non-regular Regions and the Use of Standard Desings for Generalized Linear Models
The Design of an experiment involves selection of levels of one or more factor in order to optimize one or more criteria such as prediction variance or parameter variance criteria. Good experimental designs will have several desirable properties. Typically, one can not achieve all the ideal …