Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 12 of 12 for “"FPCA"”.
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Likelihood Ratio Combination of Multiple Biomarkers and Change Point Detection in Functional Time Series
… researchers. Existing methods either rely on FPCA, which may perform poorly with complex data, or use bootstrap approaches in forms that fall short in effectively detecting diverse change functions. In our study, we propose a novel self-normalized test for functional time series implemented …
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EXTRACT NESSENTIAL FACTORS FROM HIGH DIMENSIONAL BIG DATA
… data. Secondly, we proposed 3D Image FPCA to extract factors from 3-dimensional functional MRI data. The proposed methods display superior performance compared to conventional methods.
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A percepção do familiar sobre os cuidados paliativos exclusivos no centro de terapia intensiva pediátrica oncológica: uma abordagem fenomenológica
… na criança, fora de possibilidade de cura atual (FPCA), hospitalizada no centro de terapia intensiva pediátrica oncológica; Desvelar até que ponto a família é orientada para os cuidados paliativos exclusivos no CTIPO; Analisar a luz de Merleau-Ponty como é a percepção da família da criança com …
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Mutual Coupling Reduction Techniques for Multi-band Base Station Antennas
… HB operating band. A Fabry-Perot cavity antenna (FPCA) operating in the second resonance mode (N=1) is proposed to improve this attenuation through increasing the boresight directivity. Upon HB excitation of the proposed FPCA, circular-disk MB parasitics, necessary for broadband matching of the …
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A functional data analytic approach for region level differential DNA methylation detection
… functional principal component analysis (FPCA) and smoothed functional principal component analysis (SFPCA), to identify differentially methylated regions (DMRs) that will enable discovery of epigenomic structural variations in NGS data. Using real and simulated data, the performance of …
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Longitudinal analysis of platelet count data
… zones. Functional principal component analysis (FPCA) further confirmed these seasonal patterns and revealed inter-year variability. Critical to this study is the identification of two primary donor clusters, one with stable or elevated platelet counts and another showing a declining trend …
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Statistical Methods for In-session Hemodialysis Monitoring
… properties of the covariance of mixed data and FPCA. Simulation studies shows that our method is applicable to small sample size with proper power and size control. Meanwhile, to locate regions that contribute most to significant difference between two groups of univariate functional data, we …
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Statistical Methods for Multivariate Functional Data Clustering, Recurrent Event Prediction, and Accelerated Degradation Data Analysis
… the functional principal component analysis (FPCA), and use a model based clustering method on a transformed matrix. A penalty term is imposed on the likelihood so that variable selection is performed automatically. In Chapter 3, we propose a covariate-adjusted model to predict next event in a …
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Hyperspectral image compression using implicit neural representations
… X264, (7) X265, (8) PCA-X264, (9) PCA-X265, (10) FPCA-JPEG2000, (11) 3D-DCT, (12) 3D-DWT-SVR, (13) WSRC, (14) HEVC, (15) RPM, and (16) 3D-SPECK.) for hyperspectral image compression, and according to the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics, the …
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Novel statistical modeling of sleep patterns in Drosophila melanogaster
… from functional principal component analysis (FPCA). Once the sleep features are derived, the next step in the framework consists of using two different types of statistical models (multiple linear regression and Cox proportional hazards regression) paired with two different model selection …
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Algorithms for Sparse and Low-Rank Optimization: Convergence, Complexity and Applications
… robust and powerful algorithm, which we call FPCA (Fixed Point Continuation with Approximate SVD), that can solve very large matrix rank minimization problems. Our numerical results on randomly generated and real matrix completion problems demonstrate that this algorithm is much faster and …
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Functional Linear Regression in High Dimensions
Functional linear regression has occupied a central position in the area of functional data analysis, and attracted substantial research attention in the past decade. With increasingly complex data of this type collected in modern experiments, we conduct further investigations in response to the …