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Nonoptimally Weighted Least Squares
The American Statistician, 1988Abstract This research was supported in part by National Institutes of Health Grants AR20610 (Multipurpose Arthritis Center) and 2R01GM21215–12 (Adaptation of New Statistical Ideas for Medicine) awarded to Stanford University. Most of the ideas here originate with John Tukey, in published or unpublished work. The authors indicate that it was especially
Daniel A. Bloch, Lincoln E. Moses
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Weighted Least Squares Fitting Using Ordinary Least Squares Algorithms
Psychometrika, 1997A general approach for fitting a model to a data matrix by weighted least squares (WLS) is studied. This approach consists of iteratively performing (steps of) existing algorithms for ordinary least squares (OLS) fitting of the same model. The approach is based on minimizing a function that majorizes the WLS loss function.
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Optimized sample-weighted partial least squares
Talanta, 2007In ordinary multivariate calibration methods, when the calibration set is determined to build the model describing the relationship between the dependent variables and the predictor variables, each sample in the calibration set makes the same contribution to the model, where the difference of representativeness between the samples is ignored.
Lu, Xu +6 more
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Weighted-average least squares: Improvements and extensions
The Stata Journal: Promoting communications on statistics and StataThis article presents version 3.0 of the wals command, which implements the weighted-average least-squares estimator of Magnus, Powell, and Prüfer (2010, Journal of Econometrics 154: 139–153).
Luca, Giuseppe De, Magnus, Jan R.
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Weighted Least-Squares Smoothing Filters
IRE Transactions on Circuit Theory, 1955This paper differs from many others on least square filtering, in that no explicit note has been taken of the noise spectrum, at least no more than is taken when one fits a curve of least squares to a set of data. The concept of a least weighted error, not new in curve fitting, has, to the author's knowledge, never before been applied to a filter ...
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On the weighting method for mixed least squares–total least squares problems
Numerical Linear Algebra with Applications, 2017SummaryIt is well known that the standard algorithm for the mixed least squares–total least squares (MTLS) problem uses the QR factorization to reduce the original problem into a standard total least squares problem with smaller size, which can be solved based on the singular value decomposition (SVD).
Qiaohua Liu, Minghui Wang
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Weighted least-squares blind deconvolution
1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999This paper presents a new cost function for blind deconvolution of non-minimum phase systems. The proposed criterion arises as a natural consequence of a fundamental theorem proved by Benveniste, Goursat and Ruget (1980), and appears to be the weighted square of the difference among two spectra, thus its minimization leads to a weighted least-squares ...
S. Fiori, F. Piazza
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Weighted least square ensemble networks
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 2003Ensemble of networks has been proven to give better prediction result than a single network. Two commonly used methods of determining the ensemble weights are simple average ensemble method and the generalized ensemble method. In the paper, we propose a weighted least square ensemble network.
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LEAST-SQUARE SOLUTIONS WITH WEIGHTS
Survey Review, 1943AbstractThe modern abridged method of solution as applied to observation equations was given in an earlier number of this Review. The present article, applying the same abridged method to the solution of conditioned equations, shows how the weights of any functions of the adjusted values can be obtained as well as the corrections themselves.
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