Results 161 to 170 of about 63,550 (174)

Spectral semi-blind deconvolution with least trimmed squares regularization

Infrared Physics & Technology, 2014
Abstract A spectral semi-blind deconvolution with least trimmed squares regularization (SBD-LTS) is proposed to improve spectral resolution. Firstly, the regularization term about the spectrum data is modeled as the form of least trimmed squares, which can help to preserve the peak details better. Then the regularization term about the PSF is modeled
Lizhen Deng, Hu Zhu
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Partial least trimmed squares regression

Chemometrics and Intelligent Laboratory Systems, 2022
Zhonghao Xie, Xi'an Feng, Xiaojing Chen
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Robust Collaborative Recommendation by Least Trimmed Squares Matrix Factorization

2010 22nd IEEE International Conference on Tools with Artificial Intelligence, 2010
Collaborative filtering (CF) recommender systems help people discover what they really need in a large set of alternatives by analyzing the preferences of other related users. Recent research has shown that the accuracy of recommendations can be improved significantly by using matrix factorization (MF) models.
Zunping Cheng, Neil Hurley
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The influence functions for the least trimmed squares and the least trimmed absolute deviations estimators

Statistics & Probability Letters, 1994
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Nonlinear robust modeling base on least trimmed squares regression

2008 7th World Congress on Intelligent Control and Automation, 2008
Due to low breakdown point of existing nonlinear robust modeling algorithms, a novel robust modeling algorithm based on least trimmed squares is proposed. This algorithm is based on linear least trimmed squares regression. Confidence interval of normal distribution is used to select outliers, and least square support vector machine regression is ...
null Bao Xin, null Dai Liankui
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Least trimmed squares in nonlinear regression under dependence

Journal of Statistical Planning and Inference, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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
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The feasible solution algorithm for least trimmed squares regression

Computational Statistics & Data Analysis, 1994
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Least Median of Squares (LMS) and Least Trimmed Squares (LTS) Fitting for the Weighted Arithmetic Mean

2018
We look at different approaches to learning the weights of the weighted arithmetic mean such that the median residual or sum of the smallest half of squared residuals is minimized. The more general problem of multivariate regression has been well studied in statistical literature, however in the case of aggregation functions we have the restriction on ...
Gleb Beliakov, Marek Gagolewski, Simon James   +2 more
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A note on the breakdown point of the least median of squares and least trimmed squares estimators

Statistics & Probability Letters, 1993
A notion of d-fullness is introduced to study a robust extension of the maximum likelihood principle. Some results about the breakdown point of existing robust estimators follow.
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