184 research outputs found

    Pairwise Meta-Modeling of Multivariate Output Computer Models Using Nonseparable Covariance Function

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    <div><p></p><p>The Gaussian process (GP) model is a popular method for emulating deterministic computer simulation models. Its natural extension to computer models with multivariate outputs employs a multivariate Gaussian process (MGP) framework. Nevertheless, with significant increase in the number of design points and the number of model parameters, building a MGP model is a very challenging task. Under a general MGP model framework with nonseparable covariance functions, we propose an efficient meta-modeling approach featuring a pairwise model building scheme. The proposed method has excellent scalability even for a large number of output levels. Some properties of the proposed method have been investigated and its performance has been demonstrated through several numerical examples. Supplementary materials of this paper are available online. </p></div

    Association between pseudodrusen and incidence of late age-related macular degeneration (AMD) in the fellow eye.

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    <p>Association between pseudodrusen and incidence of late age-related macular degeneration (AMD) in the fellow eye.</p

    The prevalence rate of pseudodrusen in the fellow eye of patients with unilateral neovascular age-related macular degeneration.

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    <p>The prevalence rate of pseudodrusen in the fellow eye of patients with unilateral neovascular age-related macular degeneration.</p

    The Selection of Studies for the Meta-analysis.

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    <p>The Selection of Studies for the Meta-analysis.</p

    Association between pseudodrusen and incidence of the neovascular age-related macular degeneration (nAMD) in the fellow eye.

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    <p>Association between pseudodrusen and incidence of the neovascular age-related macular degeneration (nAMD) in the fellow eye.</p

    Pairwise Estimation of Multivariate Gaussian Process Models With Replicated Observations: Application to Multivariate Profile Monitoring

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    <p>Profile monitoring is often conducted when the product quality is characterized by profiles. Although existing methods almost exclusively deal with univariate profiles, observations of multivariate profile data are increasingly encountered in practice. These data are seldom analyzed in the area of statistical process control due to lack of effective modeling tools. In this article, we propose to analyze them using the multivariate Gaussian process model, which offers a natural way to accommodate both within-profile and between-profile correlations. To mitigate the prohibitively high computation in building such models, a pairwise estimation strategy is adopted. Asymptotic normality of the parameter estimates from this approach has been established. Comprehensive simulation studies are conducted. In the case study, the method has been demonstrated using transmittance profiles from low-emittance glass. Supplementary materials for this article are available online.</p

    10% SDS-PAGE analysis of the purification fused GDH-NOX.

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    <p>Line 1: protein marker; Lane 2: purified GDH-NOX with His-tag.</p
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