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We consider a diffusion process (X <Subscript> t </Subscript>)<Subscript> t ≥ 0</Subscript>, with drift b(x) and diffusion coefficient σ(x). At discrete times t <Subscript> k </Subscript> = k δ for k from 1 to M, we observe noisy data of the sample path, <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$${Y_{k\delta}=X_{k\delta}+\varepsilon_{k}}$$</EquationSource > </InlineEquation> . The random variables <InlineEquation ID="IEq2"> <EquationSource...</equationsource></inlineequation></equationsource></inlineequation></subscript></subscript></subscript>
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Penalties with an ℓ1 norm provide solutions in which some coefficients are exactly zero and can be used for selecting variables in regression settings. When applied to the logistic regression model, they also can be used to select variables which affect classification. We focus on the form of...
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In sparse regression modeling via regularization such as the lasso, it is important to select appropriate values of … tuning parameters including regularization parameters. The choice of tuning parameters can be viewed as a model selection and … evaluation problem. Mallows’ Cp type criteria may be used as a tuning parameter selection tool in lasso type regularization …
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For the vast majority of genome-wide association studies (GWAS) statistical analysis was performed by testing markers individually. Elementary statistical considerations clearly show that in the case of complex traits an approach based on multiple regression or generalized linear models is...
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