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        <datestamp>2023-07-11</datestamp>
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          <dc:description>Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01</dc:description>
          <dc:description>The student, Huiqin Xin, accepted the attached license on 2022-04-18 at 20:55.</dc:description>
          <dc:description>The student, Huiqin Xin, submitted this Dissertation for approval on 2022-04-18 at 21:03.</dc:description>
          <dc:description>This Dissertation was approved for publication on 2022-04-22 at 07:53.</dc:description>
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          <dc:contributor>Zhao, Sihai Dave</dc:contributor>
          <dc:contributor>Zhao, Sihai Dave</dc:contributor>
          <dc:contributor>Liang, Feng</dc:contributor>
          <dc:contributor>Chatterjee, Sabyasachi</dc:contributor>
          <dc:contributor>Wang, Shulei</dc:contributor>
          <dc:date>2022-05</dc:date>
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          <dc:title>Simultaneous multiparameter estimation</dc:title>
          <dc:creator>Xin, Huiqin</dc:creator>
          <dc:date>2022-04-22</dc:date>
          <dc:subject>Compressive sensing</dc:subject>
          <dc:subject>Compound decision theory</dc:subject>
          <dc:subject>Machine learning</dc:subject>
          <dc:description>Simultaneously estimating a large amount of parameters is a common problem in statistics. We investigate two cases of simultaneous multiparameter estimation. In the first case, data are generated directly by target parameters. Our research focuses on high-dimensional covariance matrix estimation problem. We introduce two empirical Bayes approaches, compound decision approach and regression approach, to solve this problem. In both approaches, we vectorize the covariance matrices and approximate the optimal decision rule in a broad class of rules. In the second case, data are generated by a function of the target parameters with addictive observation noise. In particular, we study the linear model where the target nonnegative sparse vector is transformed to noisy observations by a measurement matrix. Specifically, the designed measurement matrix is corrupted in data generation. We investigate the behavior of matrix uncertainty selector in the corrupted matrix setting and weakened its condition with nonnegativity constraints.</dc:description>
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          <dc:language>eng</dc:language>
          <dc:identifier>https://hdl.handle.net/2142/115577</dc:identifier>
          <dc:rights>Copyright 2022 Huiqin Xin</dc:rights>
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            <name>Ph.D.</name>
            <level>Dissertation</level>
            <discipline>Statistics</discipline>
            <grantor>University of Illinois at Urbana-Champaign</grantor>
            <department>Statistics</department>
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