Results 11 to 20 of about 544,201 (272)
A model where the least trimmed squares estimator is maximum likelihood
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2023 The least trimmed squares (LTS) estimator is a popular robust regression estimator. It finds a subsample of h ‘good’ observations among n observations and applies least squares on that subsample.
Vanessa Berenguer-Rico +2 more
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Communications in Statistics - Theory and Methods, 2023 We provide Bayesian inference in the context of Least Median of Squares and Least Trimmed Squares, two well-known techniques that are highly robust to outliers.
M. Tsionas
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Modeling the proportion of measles cases using sparse least trimmed squares
JURNAL PIJAR MIPA, 2023 Measles is a highly contagious disease and a health problem in several countries, including Indonesia. In 2022, Indonesia will experience an extraordinary situation (KLB) of measles cases, with the number of measles cases reaching 3,341 across 223 ...
S. Pulungan, Rina Filia Sari
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A Novel Reconstruction Method for Measurement Data Based on MTLS Algorithm
Sensors, 2020 Reconstruction methods for discrete data, such as the Moving Least Squares (MLS) and Moving Total Least Squares (MTLS), have made a great many achievements with the progress of modern industrial technology.
Tianqi Gu +3 more
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Computation of least squares trimmed regression--an alternative to least trimmed squares regression
, 2023 The least squares of depth trimmed (LST) residuals regression, proposed in Zuo and Zuo (2023) \cite{ZZ23}, serves as a robust alternative to the classic least squares (LS) regression as well as a strong competitor to the famous least trimmed squares (LTS) regression of Rousseeuw (1984) \cite{R84}.
Zuo, Yijun, Zuo, Hanwen
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Sparse Least Trimmed Squares Regression [PDF]
SSRN Electronic Journal, 2011 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. This paper combines robust regression and sparse model estimation.
Andreas Alfons +2 more
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Robust hybrid algorithms for regularization and variable selection in QSAR studies
Journal of Nigerian Society of Physical Sciences, 2023 This study introduces a robust hybrid sparse learning approach for regularization and variable selection. This approach comprises two distinct steps.
Christian N. Nwaeme, Adewale F. Lukman
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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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Modeling the Proportion of Tuberculosis Cases in South Sulawesi using Sparse Least Trimmed Squares
EKSAKTA: Journal of Sciences and Data Analysis, 2022 The deadliest infectious disease in Indonesia is tuberculosis (TB), and South Sulawesi is one of the provinces that contributed the most tuberculosis cases in Indonesia in 2018 with 84 cases per 100,000 population.
Trigarcia Maleachi Randa +2 more
semanticscholar +1 more source
Shield Tunnel Convergence Diameter Detection Based on Self-Driven Mobile Laser Scanning
Remote Sensing, 2022 The convergence diameter of shield tunnels is detected by ellipse fitting or local curve fitting to cross-section points. However, the tunnel section, which is extruded by an external force, has an irregular elliptical shape, and the waist of the tunnel ...
Lei Xu +6 more
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