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The Global ETD Search service is a free service for researchers
to find electronic theses and dissertations. This service is
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Networked Digital Library of Theses and Dissertations.
Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
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Showing 1 to 10 of 953 (0.034 seconds)Spelling suggestions: "subject:"[een] LEAST SQUARES"" "subject:"[enn] LEAST SQUARES""
| 1 |
Multivariable inferential estimationNasir, Imtiaz Hussain January 2003 (has links)
No description available.
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| 2 |
Least squares and adaptive multirate filtering /Hawes, Anthony H. January 2003 (has links) (PDF)
Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 2003. / Thesis advisor(s): Charles W. Therrien, Roberto Cristi. Includes bibliographical references (p. 45). Also available online.
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| 3 |
Some numerical computations in linear estimationBhattacharya, Binay K. January 1978 (has links)
No description available.
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| 4 |
An empirical investigation of nonlinear least squares estimation with correlated errorsAdelaar, Glenn A. 08 1900 (has links)
No description available.
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| 5 |
The consistency of differential and integral thermonuclear neutronics dataReupke, William Albert 12 1900 (has links)
No description available.
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| 6 |
Numerical methods for box-constrained integer least squares problemsYang, Xiaohua, January 1900 (has links)
Thesis (Ph.D.). / Written for the School of Computer Science. Title from title page of PDF (viewed 2008/03/12). Includes bibliographical references.
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| 7 |
Iteratively Reweighted Least Squares Minimization With Prior Information A New ApproachPopov, Dmitriy 01 January 2011 (has links)
Iteratively reweighted least squares (IRLS) algorithms provide an alternative to the more standard 1 l -minimization approach in compressive sensing. Daubechies et al. introduced a particularly stable version of an IRLS algorithm and rigorously proved its convergence in 2010. They did not, however, consider the case in which prior information on the support of the sparse domain of the solution is available. In 2009, Miosso et al. proposed an IRLS algorithm that makes use of this information to further reduce the number of measurements required to recover the solution with specified accuracy. Although Miosso et al. obtained a number of simulation results strongly confirming the utility of their approach, they did not rigorously establish the convergence properties of their algorithm. In this paper, we introduce prior information on the support of the sparse domain of the solution into the algorithm of Daubechies et al. We then provide a rigorous proof of the convergence of the resulting algorithm.
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| 8 |
The effect of autocorrelated errors on various least square estimators /Hong, Dun-Mow,1938- January 1971 (has links)
No description available.
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| 9 |
Line intensities and half widths of the H₂O v₂ band near 2000cm⁻¹ obtained by using a least squares fit method /Chang, Yoon Samuel January 1976 (has links)
No description available.
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| 10 |
Equality of minimum variance unbiased estimator under two different modelsToh, Keng Choo. January 1975 (has links)
No description available.
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