Results 211 to 220 of about 16,285 (243)

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 ...
Chuanfa Chen, Changqing Yan
exaly   +2 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   +2 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, Xuebao Wang
exaly   +2 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.
Raghu Krishnapuram, Rajesh N Dave
exaly   +2 more sources

Partial least trimmed squares regression

Chemometrics and Intelligent Laboratory Systems, 2022
Zhonghao Xie
exaly   +2 more sources

Study on least trimmed squares fuzzy neural networks

2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, 2010
In this paper, least trimmed squares (LTS) estimators, frequently used in robust (or resistant) linear parametric regression problems, will be generalized to nonparametric LTS-fuzzy neural networks (LTS-FNNs) for nonlinear regression problems. Emphasis is put particularly on the robustness against outliers.
Hsu-Kun Wu, Jer-Guang Hsieh, Ker-Wei Yu
openaire   +1 more source

Trimmed diffusion least mean squares for distributed estimation

2015 IEEE International Conference on Digital Signal Processing (DSP), 2015
We consider the problem of distributed estimation, where a set of nodes is required to collectively estimate network parameters from noisy measurements. The problem is important when modeling a wide class of real-time sensor networks, where efficiency, robustness, and low power consumption are desired features. In this work, we focus on diffusion-based
Hong Ji, Xiaohan Yang, Badong Chen
openaire   +1 more source

Computing least trimmed squares regression with the forward search

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   +1 more source

Least Trimmed Squares for Regression Models with Stable Errors

Fluctuation and Noise Letters, 2023
Least Trimmed Squares (LTS) is a robust regression method with respect to outliers. It is based on performing Ordinary Least Squares (OLS) estimates on sub-datasets and determining the optimal solution corresponding to the minimum sum of squared residuals. Since the method of LTS is based on OLS, errors in regression models have finite variance.
Mohammad Bassam Shiekh Albasatneh, Adel Mohammadpour   +1 more
openaire   +1 more source