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 8 of 8 for “"Sufficient dimension reduction"”.
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Sufficient Dimension Reduction with Missing Data
Existing sufficient dimension reduction (SDR) methods typically consider cases with no missing data. The dissertation aims to propose methods to facilitate the SDR methods when the response can be missing. The first part of the dissertation focuses on the seminal sliced inverse regression (SIR) …
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Model-Free Variable Selection through Sufficient Dimension Reduction
… the natural connection between the fields of sufficient dimension reduction and variable selection to develop new theory and methods for model-free variable selection. After developing the natural connection between sufficient dimension reduction and model-free variable selection we introduce …
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Sparse group sufficient dimension reduction and covariance cumulative slicing estimation
… (Friedman et al., 2010) to the framework of the sufficient dimension reduction. We propose a method called the sparse group sufficient dimension reduction (sgSDR) to conduct group and within group variable selections simultaneously without assuming a specific model structure on the regression …
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Dimension reduction and efficient recommender system for large-scale complex data
… thesis, we address three challenging issues: sufficient dimension reduction for longitudinal data, nonignorable missing data with refreshment samples, and large-scale recommender systems. In the first part of this thesis, we incorporate correlation structure in sufficient dimension reduction …
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On testing common indices for several multi-index models: A link-free approach
<p>"To avoid the curse of dimensionality, and to help us better understand the structure of the high dimensional data, methods for dimension reduction are clearly called for. The common linear dimension reduction techniques for single population include principal component analysis (PCA) which is …
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Semiparametric Approaches for Dimension Reduction Through Gradient Descent on Manifold
High-dimensional data arises at an unprecedented speed across various fields. Statistical models might fail on high-dimensional data due to the "curse of dimensionality". Sufficient dimension reduction (SDR) is to extract the core information through low-dimensional mapping so that efficient …
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Dimension reduction methods for quantifying local variable importance and the statistical analysis of network data
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01