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Differentially Private Ordinary Least Squares
The Journal of Privacy and Confidentiality, 2019 Linear regression is one of the most prevalent techniques in machine learning; however, it is also common to use linear regression for its explanatory capabilities rather than label prediction.
Or Sheffet
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Non-parametric and least squares Langley plot methods [PDF]
Atmospheric Measurement Techniques, 2016 Langley plots are used to calibrate sun radiometers primarily for the measurement of the aerosol component of the atmosphere that attenuates (scatters and absorbs) incoming direct solar radiation.
P. W. Kiedron, J. J. Michalsky
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Separations, 2018 It is necessary to determine the limit of detection when validating any analytical method. For methods with a linear response, a simple and low labor-consuming procedure is to use the linear regression parameters obtained in the calibration to estimate ...
Juan M. Sanchez
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Total Least Squares Registration of 3D Surfaces [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013 Co-registration of point clouds of partially scanned objects is the first step of the 3D modeling workflow. The aim of coregistration is to merge the overlapping point clouds by estimating the spatial transformation parameters.
U. Aydar +3 more
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On the Least Median Square Problem [PDF]
Discrete & Computational Geometry, 2004 We consider the exact and approximate computational complexity of the multivariate least median-of-squares (LMS) linear regression estimator. The LMS estimator is among the most widely used robust linear statistical estimators. Given a set of n points in ${\Bbb R}^d$ and a parameter k, the problem is equivalent to computing the narrowest slab bounded ...
Jeff Erickson 0001 +2 more
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Teaching Least Squares in Matrix Notation
Ratio Mathematica, 2017 Material for teaching least squares at the undergraduate level in matrix notation is reported. The weighted least squares equations are first derived in matrix form; equivalence with the standard results obtained by standard algebra are then given for ...
Guglielmo Monaco, Aniello Fedullo
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The performance of unweighted least squares and regularized unweighted least squares in estimating factor loadings in structural equation modeling [PDF]
International Journal of Data and Network Science, 2023 In a confirmatory study, researchers are expected to employ the covariance-based structural equation modeling (CB-SEM). One of the key presumptions when utilizing CB-SEM is that the data is multivariate normal.
Nurul Raudhah Zulkifli +2 more
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Least Squares for Practitioners [PDF]
Mathematical Problems in Engineering, 2010 In experimental science and engineering, least squares are ubiquitous in analysis and digital data processing applications. Minimizing sums of squares of some quantities can be interpreted in very different ways and confusion can arise in practice, especially concerning the optimality and reliability of the results.
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, 2000 Least trimmed squares (LTS) is a statistical technique for estimation of unknown parameters of a linear regression model and provides a “robust” alternative to the classical regression method based on minimizing the sum of squared residuals.
Čížek, Pavel, Víšek, Jan Ámos
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On the Least Trimmed Squares Estimator [PDF]
Algorithmica, 2012 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
David M. Mount +4 more
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