Robust Methods for Data Reduction

Farcomeni, Alessio; Greco, Luca

doi:10.1201/b18358

Cited by 62 publications

(42 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Another crucial point is that outliers may be present in the observed data, and this poses questions on the reliability of mean regression estimates. In these cases, one could use robust regression approaches (see [34,35,57], for reviews) or focus on the median of the outcome distribution. Quantile regression, additionally, allows the user to avoid transformations of the outcome in many cases, making parameter estimates more readily interpretable.…”

Section: Introductionmentioning

confidence: 99%

Linear quantile regression models for longitudinal experiments: an overview

Marino

Farcomeni

2015

METRON

Self Cite

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Linear quantile regression models for longitudinal experiments: an overview

Marino

Farcomeni

2015

METRON

Self Cite

View full text Add to dashboard Cite

“…There are several approaches to robust clustering that are based on trimming (see, e.g., Cuesta-Albertos et al (1997), Hennig (2003), Gallegos and Ritter (2005), Neykov et al (2007), García-Escudero et al (2008) and other references included in García-Escudero et al (2010)). For a detailed review, see Farcomeni and Greco (2015) and Ritter (2014). Robust clustering methods based on trimming return a fraction 1 − α 0 of outlierfree observations which are assigned to the different clusters.…”

Section: Introductionmentioning

confidence: 99%

A reweighting approach to robust clustering

et al. 2017

Self Cite

View full text Add to dashboard Cite

An iteratively reweighted approach for robust clustering is presented in this work. The method is initialized with a very robust clustering partition based on an high trimming level. The initial partition is then refined to reduce the number of wrongly discarded observations and substantially increase efficiency. Simulation studies and real data examples indicate that the final clustering solution is both robust and efficient, and naturally adapts to the true underlying contamination level.

show abstract

“…In other words, it can lead to over-optimistic eigenvalues and a high total variance explained proportions that cannot actually exist. These problems can be overcome by using robust methods for PCA [32].…”

Section: Robust Principal Component Analysis Based On MCDmentioning

confidence: 99%

Robust Principal Component Analysis based on Fuzzy Coded Data

Alkan¹,

Ganik²

2017

ANADOLU UNIVERSITY JOURNAL OF SCIENCE AND TECHNOLOGY a - Applied Sciences and Engineering

View full text Add to dashboard Cite

In the presence of outliers in the dataset, the principal component analysis method, like many of the classical statistical methods, is severely affected. For this reason, if there are outliers in dataset, researchers tend to use alternative methods. Use of fuzzy and robust approaches is the leading choice among these methods. In this study, a new approach to robust fuzzy principal component analysis is proposed. This approach combines the power of both robust and fuzzy methods at the same time and collects these two approaches under the framework of principal component analysis. The performance of proposed approach called robust principal component analysis based on fuzzy coded data is examined through a set of artificial dataset that are generated by considering three different scenarios and a real dataset to observe how it is affected by the increase in sample size and changes in the rate of outliers. In light of the study's findings, it is seen that the proposed approach gives better results than the ones in the classical and robust principal component analysis in the presence of outliers in dataset.

show abstract

Robust Methods for Data Reduction

Cited by 62 publications

References 0 publications

Linear quantile regression models for longitudinal experiments: an overview

Linear quantile regression models for longitudinal experiments: an overview

A reweighting approach to robust clustering

Robust Principal Component Analysis based on Fuzzy Coded Data

Contact Info

Product

Resources

About