Results 241 to 250 of about 2,627,626 (274)

On least trimmed squares neural networks

Neurocomputing, 2015
In this paper, least trimmed squares (LTS) estimators, frequently used in robust (or resistant) linear parametric regression problems, will be generalized to nonparametric LTS neural networks for nonlinear regression problems. Emphasis is put particularly on the robustness against outliers.
Yih-Lon Lin, Jyh-Horng Jeng, Jer-Guang Hsieh   +2 more
exaly   +3 more sources

Adaptive choice of trimming proportion in trimmed least-squares estimation

Statistics and Probability Letters, 1997
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yadolah Dodge
exaly   +3 more sources

Computing least trimmed squares regression with the forward search [PDF]

Statistics and Computing, 1999
Least trimmed squares (LTS) provides a parametric family of high breakdown estimators in regression with better asymptotic properties than least median of squares (LMS) estimators. We adapt the forward search algorithm of Atkinson (1994) to LTS and provide methods for determining the amount of data to be trimmed.
Atkinson A.C;鄭宗記, Cheng,Tsung-Chi
openaire   +3 more sources

A robust weighted least squares support vector regression based on least trimmed squares

Neurocomputing, 2015
In order to improve the robustness of the classcial LSSVM when dealing with sample points in the presence of outliers, we have developed a robust weighted LSSVM (reweighted LSSVM) based on the least trimmed squares technique (LTS). The procedure of the reweighted LSSVM includes two stages, respectively used to increase the robustness and statistical ...
Changqing Yan, Fa Chen
exaly   +3 more sources

The influence functions for the least trimmed squares and the least trimmed absolute deviations estimators

Statistics and Probability Letters, 1994
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
exaly   +3 more sources

Multivariate least-trimmed squares regression estimator

Computational Statistics and Data Analysis, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
exaly   +3 more sources

Sensor Bias Estimation Based on Ridge Least Trimmed Squares

IEEE Transactions on Aerospace and Electronic Systems, 2020
A robust sensor bias estimation approach, named as the ridge least trimmed squares (RLTS), is proposed. Combing the advantages of ridge regression and least trimmed squares, RLTS can solve the sensor bias estimation problem with the presence of misassociations and ill-conditioning. Simulation results verify the effectiveness of the proposed approach.
Wei Tian
exaly   +3 more sources

Application of the least trimmed squares technique to prototype-based clustering

Pattern Recognition Letters, 1996
Prototype-based clustering algorithms such as the K-means and the Fuzzy C-Means algorithms are sensitive to noise and outliers. This paper shows how the Least Trimmed Squares technique can be incorporated into prototype-based clustering algorithms to make them robust.
Rajesh Dave, Raghu Krishnapuram
exaly   +3 more sources

Partial least trimmed squares regression

Chemometrics and Intelligent Laboratory Systems, 2022
Zhonghao Xie, Xi'an Feng, Xiaojing Chen
exaly   +2 more sources

Combining forecasts using the least trimmed squares. [PDF]

Kybernetika, 2001
Summary: Employing a recently derived asymptotic representation of the least trimmed squares estimator, the combinations of the forecasts with constraints are studied. Under the assumption of the unbiasedness of individual forecasts it is shown that the combination without intercept and with constraints imposed on the estimate of the regression ...
Víšek, Jan Ámos
openaire   +4 more sources