Experimental designs for generalized linear models and functional magnetic resonance imaging
exact designs for GLMs, that are robust against parameter, link and model
uncertainties by improving an existing algorithm and providing a new one, based on using a continuous particle swarm optimization (PSO) and spectral clustering. The proposed algorithm is sufficiently versatile to accomodate most popular design selection criteria, and we concentrate on providing robust designs for GLMs, using the D and A optimality criterion. The second part of our research is on providing an algorithm
that is a faster alternative to a recently proposed genetic algorithm (GA) to construct optimal designs for fMRI studies. Our algorithm is built upon a discrete version of the PSO.]]>autTemkit, M'HamedthsKao, JasondgcReiser, Mark R.dgcBarber, JarrettdgcMontgomery, Douglas C.dgcPan, RongpblArizona State UniversityengPartial requirement for: Ph.D., Arizona State University, 2014Includes bibliographical references (p. 69-74)Field of study: Statisticsby M'Hamed Temkithttps://hdl.handle.net/2286/R.I.2746500Doctoral DissertationAcademic thesesvii, 74 p. : ill. (some col.)114227959781630348280153224adminIn CopyrightAll Rights Reserved2014TextStatisticsfMRIGLMsLocally optimal designsPSORobust designsspectral clusteringLinear models (Statistics)Magnetic resonance imaging--Statistical methods.Magnetic resonance imaging