Search for dissertations about: "latent variable"
Showing result 1 - 5 of 65 swedish dissertations containing the words latent variable.
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1. Latent variable models for longitudinal twin data
Abstract : Longitudinal twin data provide important information for exploring sources of variation in human traits. In statistical models for twin data, unobserved genetic and environmental factors influencing the trait are represented by latent variables. In this way, trait variation can be decomposed into genetic and environmental components. READ MORE
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2. On specification and inference in the econometrics of public procurement
Abstract : In Paper [I] we use data on Swedish public procurement auctions for internal regularcleaning service contracts to provide novel empirical evidence regarding green publicprocurement (GPP) and its effect on the potential suppliers’ decision to submit a bid andtheir probability of being qualified for supplier selection. We find only a weak effect onsupplier behavior which suggests that GPP does not live up to its political expectations. READ MORE
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3. Structured Representation Using Latent Variable Models
Abstract : Over the past two centuries the industrial revolution automated a great part of work that involved human muscles. Recently, since the beginning of the 21st century, the focus has shifted towards automating work that is involving our brain to further improve our lives. READ MORE
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4. Shared Gaussian Process Latent Variable Models
Abstract : A fundamental task in machine learning is modeling the relationship between different observation spaces. Dimensionality reduction is the task of reducing thenumber of dimensions in a parameterization of a data-set. In this thesis we areinterested in the cross-road between these two tasks: shared dimensionality reduction. READ MORE
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5. Novel variable influence on projection (VIP) methods in OPLS, O2PLS, and OnPLS models for single- and multi-block variable selection : VIPOPLS, VIPO2PLS, and MB-VIOP methods
Abstract : Multivariate and multiblock data analysis involves useful methodologies for analyzing large data sets in chemistry, biology, psychology, economics, sensory science, and industrial processes; among these methodologies, partial least squares (PLS) and orthogonal projections to latent structures (OPLS®) have become popular. Due to the increasingly computerized instrumentation, a data set can consist of thousands of input variables which contain latent information valuable for research and industrial purposes. READ MORE