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Raginsky, Maxim Lee, Jaeho 2016-03-02T19:34:20Z 2016-03-02T19:34:20Z 2015-12-04 2015-12 This thesis studies MMSE estimation on the basis of quantized noisy observations. It presents nonasymptotic bounds on MMSE regret due to quantization for two settings: (1) estimation of a scalar random variable given a quantized vector of n conditionally independent observations, and (2) estimation of a p-dimensional random vector given a quantized vector of n observations (not necessarily independent) when the full MMSE estimator has a sub-Gaussian concentration property. Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-03-02 without embargo terms The student, Jaeho Lee, accepted the attached license on 2015-12-04 at 10:53. The student, Jaeho Lee, submitted this Thesis for approval on 2015-12-04 at 10:58. This Thesis was approved for publication on 2015-12-04 at 14:15. DSpace SAF Submission Ingestion Package generated from Vireo submission #8933 on 2016-03-02 at 12:51:26 Made available in DSpace on 2016-03-02T19:34:20Z (GMT). No. of bitstreams: 2 LEE-THESIS-2015.pdf: 319936 bytes, checksum: 698a60f1b5afdf4dbcdec8ef4f877a56 (MD5) LICENSE.txt: 4206 bytes, checksum: d0ec992081dd6c5a77c79a9e7654698b (MD5) Previous issue date: 2015-12-04 application/pdf http://hdl.handle.net/2142/89048 en Copyright 2015 Jaeho Lee functional quantization nonasymptotic bounds MMSE estimation from quantized observations in the nonasymptotic regime text text Electrical & Computer Engineering Electrical & Computer Engineering University of Illinois at Urbana-Champaign Thesis M.S.