Robust ANalysis Of VAriance: An approach based on the Forward Search

Bertaccini, Bruno; Varriale, Roberta

doi:10.1016/j.csda.2006.08.010

Cited by 9 publications

(9 citation statements)

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“…The forward search was also used by Bertaccini and Varriale (2007) to detect atypical observations and the analysis of their effect in the ANOVA framework. In clustering multivariate normal data, Atkinson and Riani (2007a) introduced a forward search method based on the Mahalanobis distances of the observations.…”

Section: Forward Search Methodsmentioning

confidence: 99%

Robustness, Data Analysis, and Statistical Modeling: The First 50 Years and Beyond

Barrios

2015

CSAM

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We present a survey of contributions that defined the nature and extent of robust statistics for the last 50 years. From the pioneering work of Tukey, Huber, and Hampel that focused on robust location parameter estimation, we presented various generalizations of these estimation procedures that cover a wide variety of models and data analysis methods. Among these extensions, we present linear models, clustered and dependent observations, times series data, binary and discrete data, models for spatial data, nonparametric methods, and forward search methods for outliers. We also present the current interest in robust statistics and conclude with suggestions on the possible future direction of this area for statistical science.

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Section: Forward Search Methodsmentioning

confidence: 99%

Robustness, Data Analysis, and Statistical Modeling: The First 50 Years and Beyond

Barrios

2015

CSAM

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“…Atkinson et al (2004, p. 306) proposed a constrained balanced forward search in which the distribution of an available grouping variable in the subsample is proportional to its distribution in the entire sample. This procedure has been used in discriminant analysis (Atkinson et al, 2004, Chapter 6) and ANOVA (Bertaccini & Varriale, 2007), but it is unclear whether it has advantages over the regular forward search.…”

Section: Progressing the Forward Searchmentioning

confidence: 98%

“…Atkinson and Riani (2000) monitored interesting statistics in the context of regression analysis, Atkinson et al (2004) did this for statistical analyses assuming multivariate normal data, Bertaccini and Varriale (2007) for ANOVA, and Mavridis and Moustaki (2008) for factor analysis.…”

Section: Monitoring the Forward Searchmentioning

confidence: 99%

“…Identifying and removing the outliers facilitates assessing the magnitude of the bias. The forward search has been proven useful in regression models Downloaded by [Stony Brook University] at 01:12 20 October 2014 and generalized linear models (Atkinson & Riani, 2000); cluster analysis, discriminant analysis, and principal component analysis (Atkinson et al, 2004); factor analysis (Mavridis & Moustaki, 2008); analysis of variance (ANOVA) (Bertaccini & Varriale, 2007); and models for a mixture of categorical and continuous data (Cheng & Biswas, 2008).…”

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confidence: 99%

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Robust Mokken Scale Analysis by Means of the Forward Search Algorithm for Outlier Detection

Zijlstra¹,

Ark²,

Sijtsma³

2011

Multivariate Behavioral Research

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Exploratory Mokken scale analysis (MSA) is a popular method for identifying scales from larger sets of items. As with any statistical method, in MSA the presence of outliers in the data may result in biased results and wrong conclusions. The forward search algorithm is a robust diagnostic method for outlier detection, which we adapt here to identify outliers in MSA. This adaptation involves choices with respect to the algorithm's objective function, selection of items from samples without outliers, and scalability criteria to be used in the forward search algorithm. The application of the adapted forward search algorithm for MSA is demonstrated using real data. Recommendations are given for its use in practical scale analysis.

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“…The robust analysis variance method proposed by Bertaccini and Varriale (2006) allows the monitoring of the outliers effect in the statistic modeling process. As for that, the formation of subsets is used, where the sample units are allocated, based only on data inspection.…”

Section: Introductionmentioning

confidence: 99%

Identificação de outliers por alocação das observações através do modelo Poisson aplicado ao número de casos de Aids diagnosticados no Brasil

Nepomucena

Cirillo

2010

Semin. Cienc. Biol. Saude

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ResumoO método da análise robusta de variância proposto por Bertaccini e Varriale (2006) permite monitorar o efeito de outliers no processo de modelagem estatística. Para isso, utiliza-se a formação de subconjuntos, nos quais as unidades amostrais são alocadas, baseando-se em apenas uma inspeção dos dados. Com o propósito de estender este método para o modelo Poisson, o presente trabalho propõe monitorar o efeito de outliers no número de casos de AIDS diagnosticados no Brasil no período de 2003 a 2006. A metodologia proposta foi viável e é recomendável para dados de contagem, sendo, portanto, uma importante técnica de análise de dados para identificação de outliers em amostras, podendo ser aplicado a outros modelos generalizados com as devidas modificações na obtenção dos resíduos. Palavras-chave: AIDS. Modelo Poisson. Outliers. AbstractThe robust analysis variance method proposed by Bertaccini and Varriale (2006) allows the monitoring of the outliers effect in the statistic modeling process. As for that, the formation of subsets is used, where the sample units are allocated, based only on data inspection. With the purpose of extending this method to the Poisson model, this work intends to monitor the effect of outliers in the number of AIDS cases diagnosed in Brazil, from 2003 to 2006. The methodology proposed was viable and is recommended for counting data, being, therefore, an important data analysis technique used to identify outliers in samples. It can also be applied to other generalized models with appropriate changes in obtaining residuals.

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Robust ANalysis Of VAriance: An approach based on the Forward Search

Cited by 9 publications

References 1 publication

Robustness, Data Analysis, and Statistical Modeling: The First 50 Years and Beyond

Robustness, Data Analysis, and Statistical Modeling: The First 50 Years and Beyond

Robust Mokken Scale Analysis by Means of the Forward Search Algorithm for Outlier Detection

Identificação de outliers por alocação das observações através do modelo Poisson aplicado ao número de casos de Aids diagnosticados no Brasil

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