Robust Tests for the Equality of Variances

Brown, Morton B.; Forsythe, Alan B.

doi:10.1080/01621459.1974.10482955

Cited by 2,166 publications

(974 citation statements)

References 9 publications

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“…Forsythe, 1974b) fou,nd t'lat the RT grdllp had a ~ significantly lowerwithin-subjects variance associated with the pain threshold data th an the NT group did. Using the 1 iest employing separate error terms (Brown & Forsythe, 1974b), (Roseoe, 1975) were calculated to determine the relationship between SES and the GTAR-EA, pain threshold and endurance, the GraèeIy painfulness scale, and the effectiveness of strategies employed.…”

Section: C:imentioning

confidence: 99%

The effect of experimentally induced anxiety on the experience of pressure pain

Cornwall

Donderi

1988

Pain

125

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Section: C:imentioning

confidence: 99%

The effect of experimentally induced anxiety on the experience of pressure pain

Cornwall

Donderi

1988

Pain

125

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“…Both the Fstatistic and the Brown-Forsythe statistic indicate significance of the difference. The latter test statistic is more robust under non-normality (Brown andForsythe, 1974, Conover, Johnson andJohnson, 1981).…”

Section: Resultsmentioning

confidence: 99%

An Analysis of Private Investors' Stock Market Return Forecasts

Theissen

2003

SSRN Journal

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“…The Kruskal-Wallis test does not assume normality, however the homogeneity of variance assumption still has to be met. To test the homogeneity of variance the Levene's test can be used [32]. If the homogeneity assumption is not met, the KruskalWallis test cannot be used.…”

Section: Context-variable Identification Using a Variable Variance (Amentioning

confidence: 99%

Impact of the Context Relevancy on Ratings Prediction in a Movie-Recommender System

et al. 2013

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Original scientific paperRecommender systems are a popular and a highly researched way of helping users get to their desired content in the huge amount of available data, and services online. Understanding the situation in which users consume the items was shown to improve the recommendation process. For that reason, context-aware recommender system (CARS) employs contextual information in order to enhance the user's model and to improve the recommendations. An issue that is still open is how to decide which pieces of contextual information to acquire and how to incorporate them into CARS, since using irrelevant piece of contextual information could have a negative impact on the recommendations. We propose a methodology for detecting which pieces of contextual information contribute to explaining the variance in the ratings, based on statistical testing. We also inspect the impact of the detected relevant pieces of contextual information on the ratings prediction based on the matrix-factorization algorithm. The experiment was conducted on the MovieAT database. The results showed a significant difference in the ratings prediction using the relevant and the irrelevant pieces of contextual information. We also confirmed the positive impact of the relevant, and negative impact of the irrelevant pieces of contextual information with respect to the uncontextualized model. Key words: Personalization, Recommender systems, Context-awarenessUtjecaj relevantnosti konteksta na predvi anje ocjena u sustavu za preporuke filmova. Sustavi za preporuke (eng. recommender systems) predstavljajučest i vrlo istražen način pružanja pomoći korisnicima u svrhu pronalaska željenog sadržaja u velikoj količini dostupnih podataka i usluga. Pokazalo se da uvid u situaciju u kojoj korisnici koriste sadržaj doprinosi kvaliteti preporuka. Zbog toga, konteksta svjesni sustavi za preporuke (eng. context-aware recommender systems CARS) koriste kontekstne informacije kako bi poboljšali model korisnika i time kvalitetu preporuka. Jedan od neriješenih problema je kako odlučiti koje kontekstne informacije je potrebno sakupiti i kako ih upotrijebiti u CARSu, budući da upotreba nebitnih kontekstnih informacija može imati negativan utjecaj na kvalitetu preporuka. Mi predlažemo metodologiju za otkrivanje onih kontekstih informacija koje doprinose objašnjavanju varijabilnosti ocjena za sadržaje, utemeljenu na statističkom testiranju. Tako er, istražujemo utjecaj otkrivenog bitnog konteksta na predvi anje ocjena utemeljeno na algoritmu faktorizacije matrica. Eksperiment je proveden na bazi podataka MovieAT. Rezultati su pokazali znatnu razliku u predvi anju ocjena prilikom korištenja bitnog i nebitnog konteksta. Ujedno je potvr en i pozitivan utjecaj bitnog, odnosno negativan utjecaj nebitnog konteksta, u odnosu na sustav koji ne koristi kontekst, što upućuje na važnost i kvalitetu detekcije.

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Robust Tests for the Equality of Variances

Cited by 2,166 publications

References 9 publications

The effect of experimentally induced anxiety on the experience of pressure pain

The effect of experimentally induced anxiety on the experience of pressure pain

An Analysis of Private Investors' Stock Market Return Forecasts

Impact of the Context Relevancy on Ratings Prediction in a Movie-Recommender System

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