Results 41 to 50 of about 63,550 (174)
Sparse least trimmed squares regression for analyzing high-dimensional large data sets [PDF]
, 2013 Sparse model estimation is a topic of high importance in modern data analysis due to the increasing availability of data sets with a large number of variables. Another common problem in applied statistics is the presence of outliers in the data.
Alfons, A. (Andreas) +2 more
core +3 more sources
Trimmed Least Squares Estimation in the Linear Model [PDF]
Journal of the American Statistical Association, 1980 Abstract We consider two methods of defining a regression analog to a trimmed mean. The first was suggested by Koenker and Bassett and uses their concept of regression quantiles. Its asymptotic behavior is completely analogous to that of a trimmed mean. The second method uses residuals from a preliminary estimator.
David Ruppert, Raymond J. Carroll
openaire +1 more source
Robust Regression Estimates in the Prediction of Latent Variables in Structural Equation Models [PDF]
, 2012 The incorporation of the robust regression methods Least Median Square (LMS) and Least Trimmed Squares (LTS) is proposed in structural equation modeling.
Barroso, Lúcia Pereira +1 more
core +2 more sources
On the application of nature-inspired grey wolf optimizer algorithm in geodesy
Journal of Geodetic Science, 2020 Nowadays, solving hard optimization problems using metaheuristic algorithms has attracted bountiful attention. Generally, these algorithms are inspired by natural metaphors.
Yetkin M., Bilginer O.
doaj +1 more source
Zanco Journal of Humanity SciencesHypertension is a common and serious disease, and for this reason, a sample of patients was chosen from Azadi Teaching Hospital in Duhok. In this study a comparison was made between Ordinary Least Squares (OLS) with two robust methods Least Trimmed ...
Ismat Mousa Ibrahim
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Remote Sensing, 2020 Three-dimensional light detection and ranging (LiDAR) point clouds acquired from unmanned aerial vehicles (UAVs) represent a relatively new type of remotely sensed data.
Karel Kuželka +2 more
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Revstat Statistical Journal, 2007 We consider the problem of identifying multiple outliers in linear regression models. We propose a penalized trimmed squares (PTS) estimator, where penalty costs for discarding outliers are inserted into the loss function.
G. Zioutas +2 more
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Sensors, 2011 Modern information fusion systems essentially associate decision-making processes with multi-sensor systems. Precise decision-making processes depend upon aggregating useful information extracted from large numbers of messages or large datasets ...
Jin-Tsong Jeng, You-Min Jau, Kuo-Lan Su
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BSA - exact algorithm computing LTS estimate
, 2010 The main result of this paper is a new exact algorithm computing the estimate given by the Least Trimmed Squares (LTS). The algorithm works under very weak assumptions.
Agulló +13 more
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Weighted Least Squares Regression with the Best Robustness and High Computability
AxiomsA novel regression method is introduced and studied. The procedure weights squared residuals based on their magnitude. Unlike the classic least squares which treats every squared residual as equally important, the new procedure exponentially down-weights
Yijun Zuo, Hanwen Zuo
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