Multivariate least-trimmed squares regression estimator

Jung, Kang-Mo

doi:10.1016/j.csda.2004.01.008

Cited by 16 publications

(5 citation statements)

References 6 publications

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“…Another recent paper also introduced the MLTS regression estimator [13]. In contrast to our work, this paper does not provide any theoretical results like consistency, influence functions, and asymptotic variances.…”

Section: Discussioncontrasting

confidence: 66%

The multivariate least-trimmed squares estimator

Agulló

Croux

Aelst

2008

Journal of Multivariate Analysis

113

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In this paper we introduce the least-trimmed squares estimator for multivariate regression. We give three equivalent formulations of the estimator and obtain its breakdown point. A fast algorithm for its computation is proposed. We prove Fisher-consistency at the multivariate regression model with elliptically symmetric error distribution and derive the influence function. Simulations investigate the finite-sample efficiency and robustness of the estimator. To increase the efficiency of the estimator, we also consider a one-step reweighted estimator.

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Section: Discussioncontrasting

confidence: 66%

The multivariate least-trimmed squares estimator

Agulló

Croux

Aelst

2008

Journal of Multivariate Analysis

113

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“…The performance of our algorithm has been evaluated against performance of widely known and used regression algorithms namely, KNN (Han and Kamber, 2000), Simple Linear (Seber and Lee, 1999), RBF (Haykin, 2009) and LMS (Jung, 2005). These algorithms were selected as they were shown to perform better than several other algorithms in a recent study -PAGER (Desai et al, 2010).…”

Section: Resultsmentioning

confidence: 99%

BINGR: Binary Search based Gaussian Regression

Dubey

Bharambe

Pudi

2012

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

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Regression is the study of functional dependency of one variable with respect to other variables. In this paper we propose a novel regression algorithm, BINGR, for predicting dependent variable, having the advantage of low computational complexity. The algorithm is interesting because instead of directly predicting the value of the response variable, it recursively narrows down the range in which response variable lies. BINGR reduces the computation order to logarithmic which is much better than that of existing standard algorithms. As BINGR is parameterless, it can be employed by any naive user. Our experimental study shows that our technique is as accurate as the state of the art, and faster by an order of magnitude. PROBLEM FORMULATIONIn this section, we present the mathematical model 258 Dubey H., Bharambe S.

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“…Notice from Table 14 and Fig 4(a) that that observations number 22, 46–48, 51–52, 56, and 58–62 are out-of-control points. According to Rousseeuw et al [ 31 ] and Jung [ 32 ], these out-of-control points are said to be observations produced from different woods and different pulping process from those of other observations. The details of these out-of-control points are given below: From the source of the data, it was found that all but the last four pulp samples (observations 59–62) were produced from fir wood.…”

Section: Numerical Implementation and Real Life Data Application Of mentioning

confidence: 99%

A Control Chart Based on Cluster-Regression Adjustment for Retrospective Monitoring of Individual Characteristics

Ong

Alih

2015

PLoS ONE

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The tendency for experimental and industrial variables to include a certain proportion of outliers has become a rule rather than an exception. These clusters of outliers, if left undetected, have the capability to distort the mean and the covariance matrix of the Hotelling’s T 2 multivariate control charts constructed to monitor individual quality characteristics. The effect of this distortion is that the control chart constructed from it becomes unreliable as it exhibits masking and swamping, a phenomenon in which an out-of-control process is erroneously declared as an in-control process or an in-control process is erroneously declared as out-of-control process. To handle these problems, this article proposes a control chart that is based on cluster-regression adjustment for retrospective monitoring of individual quality characteristics in a multivariate setting. The performance of the proposed method is investigated through Monte Carlo simulation experiments and historical datasets. Results obtained indicate that the proposed method is an improvement over the state-of-art methods in terms of outlier detection as well as keeping masking and swamping rate under control.

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Multivariate least-trimmed squares regression estimator

Cited by 16 publications

References 6 publications

The multivariate least-trimmed squares estimator

The multivariate least-trimmed squares estimator

BINGR: Binary Search based Gaussian Regression

A Control Chart Based on Cluster-Regression Adjustment for Retrospective Monitoring of Individual Characteristics

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