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https://olympias.lib.uoi.gr/jspui/handle/123456789/14922Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Mavridis, D. | en |
| dc.contributor.author | Moustaki, I. | en |
| dc.date.accessioned | 2015-11-24T17:44:33Z | - |
| dc.date.available | 2015-11-24T17:44:33Z | - |
| dc.identifier.issn | 0027-3171 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/14922 | - |
| dc.rights | Default Licence | - |
| dc.subject | covariance structure-analysis | en |
| dc.subject | structural equation models | en |
| dc.subject | multivariate location | en |
| dc.subject | multiple outliers | en |
| dc.subject | robust estimation | en |
| dc.subject | influential observations | en |
| dc.subject | dispersion matrices | en |
| dc.subject | estimators | en |
| dc.subject | regression | en |
| dc.subject | residuals | en |
| dc.title | Detecting outliers in factor analysis using the forward search algorithm | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | Doi 10.1080/00273170802285909 | - |
| heal.identifier.secondary | <Go to ISI>://000259640400005 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών Αγωγής. Παιδαγωγικό Τμήμα Δημοτικής Εκπαίδευσης | el |
| heal.publicationDate | 2008 | - |
| heal.abstract | In this article we extend and implement the forward search algorithm for identifying atypical subjects/observations in factor analysis models. The forward search has been mainly developed for detecting aberrant observations in regression models (Atkinson, 1994) and in multivariate methods such as cluster and discriminant analysis (Atkinson, Riani, & Cerioli, 2004). Three data sets and a simulation study are used to illustrate the performance of the forward search algorithm in detecting atypical and influential cases in factor analysis models. The first data set has been discussed in the literature for the detection of outliers and influential cases and refers to the grades of students on 5 exams. The second data set is artificially constructed to include a cluster of contaminated observations. The third data set measures car's characteristics and is used to illustrate the performance of the forward search when the wrong model is specified. Finally, a simulation study is conducted to assess various aspects of the forward search algorithm. | en |
| heal.journalName | Multivariate Behavioral Research | en |
| heal.journalType | peer-reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| mavridis-2008-Detecting outliers in.pdf | 432.3 kB | Adobe PDF | View/Open Request a copy |
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