Results 221 to 230 of about 16,285 (243)
Some of the next articles are maybe not open access.
Least Tail-Trimmed Squares for Infinite Variance Autoregressions
SSRN Electronic Journal, 2012We develop a robust least squares estimator for autoregressions with possibly heavy tailed errors. Robustness to heavy tails is ensured by negligibly trimming the squared error according to extreme values of the error and regressors. Tail‐trimming ensures asymptotic normality and super‐‐convergence with a rate comparable to the highest achieved amongst
openaire +2 more sources
A constrained least square and trimmed least square method for multisensor data fusion
International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003, 2003Though neural data fusion algorithms based on a linearly constrained least square (LCLS) method solve the ill-conditioned and singular matrix problems that arise in the LCLS method, they don't perform well when there are impulsive noises attached to several sensors.
null Haiyan Shi +2 more
openaire +1 more source
A Genetic Algorithm Implementation of the Fuzzy Least Trimmed Squares Clustering
2007 IEEE International Fuzzy Systems Conference, 2007This paper describes a new approach to finding a global solution for the fuzzy least trimmed squares clustering. The least trimmed squares (LTS) estimator is known to be a high breakdown estimator, in both regression and clustering. From the point of view of implementation, the feasible solution algorithm is one of the few known techniques that ...
Amit Banerjee, Sushil J. Louis
openaire +1 more source
An Exact Least Trimmed Squares Algorithm for a Range of Coverage Values
Journal of Computational and Graphical Statistics, 2010A new algorithm to solve exact least trimmed squares (LTS) regression is presented. The adding row algorithm (ARA) extends existing methods that compute the LTS estimator for a given coverage. It employs a tree-based strategy to compute a set of LTS regressors for a range of coverage values. Thus, prior knowledge of the optimal coverage is not required.
Hofmann, Marc H. +2 more
openaire +3 more sources
Sparse Principal Component Analysis Based on Least Trimmed Squares
Technometrics, 2019Sparse principal component analysis (PCA) is used to obtain stable and interpretable principal components (PCs) from high-dimensional data.
Yixin Wang, Stefan Van Aelst
openaire +1 more source
Robust Collaborative Recommendation by Least Trimmed Squares Matrix Factorization
2010 22nd IEEE International Conference on Tools with Artificial Intelligence, 2010Collaborative 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
openaire +1 more source
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 +2 more
openaire +1 more source
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 +2 more
openaire +1 more source
A note on the breakdown point of the least median of squares and least trimmed squares estimators
Statistics & Probability Letters, 1993A 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.
openaire +2 more sources
Adaptive least trimmed squares fuzzy neural network
2012 International conference on Fuzzy Theory and Its Applications (iFUZZY2012), 2012In this paper, we propose the adaptive least trimmed squares fuzzy neural network (ALTS-FNN), which applies the scale estimate to the least trimmed squares fuzzy neural network (LTS-FNN). The emphasis of this paper is particular on the robustness against the outliers and the choice of the trimming constant can be determined adaptively.
Jyh-Yeong Chang +2 more
openaire +1 more source
Combining forecasts using the least trimmed squares.
Kybernetika, 2001Summary: 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 ...
openaire +2 more sources